Articial intelligence in education management: Ethics and social responsibility

Juan Carlos Lázaro Guillermo, Walter Gilberto Roman Claros, Victor Tedy López

Panaifo, Rusvelth Paima Paredes, José Luis Angles Paredes, Luis Angel Guerra Grados

Panaifo, Rusvelth Paima Paredes, José Luis Angles Paredes, Luis Angel Guerra Grados,

2024

First edition: December, 2024

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Editorial Mar Caribe

Articial intelligence in education management: Ethics

and social responsibility

Colonia del Sacramento, Uruguay

2024

About the authors and the publication

Juan Carlos Lázaro Guillermo

[email protected]u.pe

hps://orcid.org/0000-0002-4785-9344

Universidad Nacional Intercultural de la Amazonía, Peru

Walter Gilberto Roman Claros

walter_roman@unu.edu.pe

hps://orcid.org/0000-0003-3069-5969

Universidad Nacional de Ucayali, Peru

Victor Tedy López Panaifo

[email protected]u.pe

hps://orcid.org/0000-0001-9893-8483

Universidad Nacional de Ucayali, Peru

Rusvelth Paima Paredes

rusv[email protected]u.pe

hps://orcid.org/0000-0001-7261-5854

Universidad Nacional de Ucayali, Peru

José Luis Angles Paredes

[email protected].pe

hps://orcid.org/0009-0003-6128-7578

Universidad Nacional Intercultural de la Amazonía, Peru

Luis Angel Guerra Grados

[email protected].pe

hps://orcid.org/0000-0001-8344-6101

Universidad Nacional Mayor de San Marcos, Perú

Book Research Result:

Original and unpublished publication, whose content is the result of a research process

conducted before its publication, has been double-blind external peer review, the book has been

selected for its scientic quality and because it contributes signicantly to the area of knowledge

and illustrates a completely developed and completed research. In addition, the publication has

gone through an editorial process that guarantees its bibliographic standardization and usability


 
Content 
Introduction ................................................................................................................................ 6 
Chapter I...................................................................................................................................... 8 
Articial Intelligence in Educational Management: Ethics and Social Responsibility8 
1 Denition and context of articial intelligence ...................................... 9 
2 Types of articial intelligence ............................................................. 10 
3 Applications of articial intelligence in educational management ...... 11 
3.1 Administrative automation ............................................................ 11 
3.2 Characterization of learning .......................................................... 11 
4 Ethical implications of articial intelligence in education .............. 12 
5 Transparency and algorithmic bias ...................................................... 13 
6 Social responsibility in the implementation of AI in education .......... 14 
6.1 Towards a more responsible future ............................................... 15 
7 Ethics and deontology of articial intelligence in the education sector 18 
7.1 Social and Cultural Eects ............................................................. 19 
7.2 Deontology and Guiding Principles .............................................. 19 
7.3 Responsibility and Accountability ................................................ 20 
7.4 Ethical Risks in the Use of AI ........................................................ 21 
8 Good Practices and Mitigation Strategies ............................................ 22 
8.1 Involvement of the Educational Community ................................. 23 
8.2 Lessons Learned and Future of AI ................................................. 24 
8.3 Towards a hybrid model ................................................................ 25 
Chapter II .................................................................................................................................. 28 
Teaching and learning with articial intelligence applications ..................................... 28 
1 Augmented and virtual reality, applications in education ................... 28 
3 Ethics, deontology and considerations in the use of AI ........................ 29 
3.1 Implications for teachers ............................................................... 29 
3.2 Personalization of learning and eciency in management ............ 31 
4 Teaching and learning with articial intelligence applications ............ 32 
4.1 Applications of Articial Intelligence in the Classroom ................ 34 


 
4.2 Long-Term Projections and the Evolution of the Teacher's Role .... 36 
4.3 AI scenario in the sociocultural context ......................................... 38 
Chapter III ................................................................................................................................ 40 
Research in education with articial intelligence: Interactive dialogic learning ....... 40 
1 Historical context of Articial Intelligence in Education ..................... 41 
1.1 Technological evolution ................................................................ 42 
1.2 Adaptive learning platforms .......................................................... 43 
1.3 Ethical and privacy concerns and inequalities in technology 
management .......................................................................................... 44 
2 Research methodologies with the application of articial intelligence 46 
3 Ethical Principles in Scientic Research .............................................. 47 
4 Context and denition of interactive dialogic learning ........................ 49 
4.1 Fundamentals of Interactive Dialogic Learning ............................. 50 
4.2 Comparison with other learning methods ..................................... 52 
4.3 Benets of Interactive Dialogic Learning and Development of 
Communication Skills ........................................................................... 52 
4.4 Promotion of critical thinking ....................................................... 53 
Chapter IV ................................................................................................................................ 55 
Ethics in scientic research .................................................................................................... 55 
1 Declaration of Helsinki .................................................................... 56 
2 Ethical challenges in specic areas of research .................................... 58 
3 The future of ethics in scientic research ............................................. 59 
4 Research as a driver of development .................................................... 62 
5 Scientic Research Methodologies ...................................................... 64 
5.1 Interdisciplinarity in Research ...................................................... 66 
5.2 Social responsibility in research ....................................................... 68 
5.3 Education and Training in Ethics ...................................................... 71 
Conclusion ................................................................................................................................ 74 
Bibliography ............................................................................................................................. 76 
 

Introduction

Ethics and social responsibility in scientic research are fundamental

aspects that ensure the integrity and development of knowledge. As science and

technology advance, so do the ethical considerations inherent in conducting

research. It is vitally important that researchers not only adhere to ethical

principles in their work, but also understand the societal implications of their

ndings.

Scientic research, whether in the medical, social, technological, or

environmental elds, can have a signicant impact on human life and society as

a whole. It is therefore essential that researchers take a responsible approach,

concerned not only with acquiring new knowledge but also with the well-being

of participants, communities and the environment. Research ethics encompasses

many issues, including obtaining informed consent, protecting privacy and

condentiality, and minimizing harm.

In addition, social responsibility means that the results of scientic

research must be disseminated and applied in ways that benet society by

promoting equitable and accessible progress for all. In this book, four chapters

are addressed, emphasizing the importance of ethics in scientic research and its

relationship with articial intelligence and social responsibility.

In chapter one, we delve into the ethics of research for the development of

knowledge and the integrity of science, emphasizing that research is carried out

in a fair and responsible manner. Therefore, a lack of ethics can have devastating

consequences, such as the spread of misinformation and a loss of trust in the

scientic community. Ethics includes not only the treatment of research

participants but also the transparency of methods, data analysis and publication

of results.

Chapter two addresses the growing dependence on technology in

education and how AI solutions are implemented in the teaching-learning

binomial, urging educational institutions to become passive recipients of

technology, ignoring important skills that cannot be replaced by digital tools.

This can include communication skills, critical thinking, and creative problem-

solving. We highlight that not all students have the same opportunities to interact

with advanced AI tools. Therefore, the lack of adequate infrastructure can lead to

growing educational inequality, so only those with sucient resources can

benet from advances in articial intelligence.

In chapter three, we delve into interactive dialogic learning from the

conceptual pedagogical approach that is based on interaction and dialogue

between participants as fundamental methods for the acquisition of knowledge.

To deeply understand this approach, it is essential to explore the theories of

learning that underpin it, the basic principles that govern it, and how they

compare to other learning methods.

Finally, in chapter four we inquire about scientic research, especially in

the area of science and technology for development, because it is not only to

promote the advancement of knowledge, but also to invest signicantly in

research and development (R+D) for greater growth and competitiveness in

management and innovation in education.

Through this research, we want to open the debate on research ethics with

appropriate regulation, as well as active engagement by researchers and

technology developers to ensure that their work contributes positively to society.

Therefore, education in research ethics is vital for the academic preparation of

students in all cycles of education, with inclusivity and equity in teaching and

learning within learning spaces.

Chapter I

Articial Intelligence in Educational Management: Ethics

and Social Responsibility

Articial intelligence (AI) has penetrated many dierent areas of modern

society, and education is no exception. In recent years, the incorporation of

articial intelligence into educational management has revealed its

transformative potential in the way educational processes are planned,

implemented and evaluated. From using algorithms to analyze large amounts of

student data to creating personalized learning platforms, articial intelligence is

presented as a tool that can signicantly improve the eciency and eectiveness

of education. But the deployment of articial intelligence technology raises

questions of ethics and social responsibility.

As automated systems are introduced that have a direct impact on

students, teachers, and administrators, it is necessary to consider the impact of

these tools on education, as well as the importance of establishing an ethical

framework to govern their use. Articial intelligence has the potential to oer

innovative solutions to classic educational problems such as personalized

learning and resource optimization. In other words, with a recommendation

system, the content can be adapted to the needs and learning speed of each

student, thus promoting a more comprehensive and eective education (Wang et

al., 2024).

Likewise, analysis tools allow educators to identify trends in student

performance, facilitating early intervention in cases of academic risk. This is how

this technological transformation raises a series of questions regarding ethics and

responsibility when implementing it. The most important aspect is the protection

of student data and privacy. When carefully collecting and analysing personal

data, educational institutions must ensure that these processes do not violate

fundamental rights and are carried out with appropriate consent and

transparency.

The lack of clear regulations in this area can lead to abuse and breaches of

condentiality, creating a lack of trust between students and families. Another

troubling issue in the debate over the ethics of articial intelligence in education

is algorithmic bias. Algorithms developed by humans may reect prejudice or

discrimination existing in society. This can lead to unfair decisions that

negatively impact certain groups of students, perpetuate inequities, and limit

opportunities for students who are already disadvantaged. That's why it's

important for edtech developers to prioritize equality and diversity when

designing their tools.

Social responsibility also plays an important role in this debate. As

educational institutions incorporate AI into their practices, they must actively

participate not only in improving education but also for the benet of students

and society as a whole. This requires a careful and critical approach to the use of

AI, ensuring that initiatives are aligned with core educational values and

promote social justice.

Looking ahead, it is important that the deployment of articial intelligence

in education is done ethically and responsibly. It is important to combine

technological innovations with a continuous analysis of their impact on society,

making education not only more eective but also more equitable and accessible

to all. This approach not only benets students, but also contributes to a more

equitable and informed society in the digital age.

1.1 Denition and context of articial intelligence

Articial intelligence (AI) is dened as a eld of study of computer science

that develops systems that perform tasks that require human intelligence. These

tasks may include learning, reasoning, problem-solving, perception, and

understanding of language. The rise of intelligence is transforming educational

management by streamlining processes and personalizing learning. The origins

of articial intelligence date back to the 50s and it was coined by John McCarthy

in 1956 during a conference at Dartmouth College, considered the cradle of this

discipline.

During these early years, articial intelligence was focused on simulating

human thought processes using computer programs. Early designs included

simple logic systems and board games that pied computers against humans. AI

has faced waiting periods followed by cycles of frustration due to a lack of

signicant progress and reduced funding (Klimczak & Petersen, 2023). In this

context, starting in the 2000s, articial intelligence began to experience a

renaissance, driven by the increase in computing power, the availability of large

amounts of data, and advances in machine learning.

AI development can be divided into several stages. The rst phase, known

as symbolic AI, focuses on manipulating symbols and rules to solve problems in

structured environments. The second phase, which emerged in the 1980s,

involved machine learning, which uses data to identify paerns and make

predictions. The third phase still in development is the era of deep articial

intelligence, which uses complex neural networks to perform more complex

tasks.

1.2 Types of articial intelligence

Articial intelligence can be classied into dierent types, depending on its

capacity and functionality. Some of the most recognized categories are:

• Narrow AI: This type of AI is designed to perform specic tasks and does

not possess general awareness or understanding. This is the specic case

of virtual assistants such as Siri or Alexa that answer questions or perform

specic tasks.

• General AI: Refers to systems that have the ability to understand, learn,

and apply knowledge in a similar way to a human. While it has yet to be

achieved, this type of AI is a long-term goal in the eld.

• Superintelligent AI: A theoretical concept that describes an intelligence

that exceeds human capacity in all creative, emotional, and problem-

solving aspects. This type of AI still belongs to the realm of science ction.

• Machine Learning: This subcategory focuses on the development of

algorithms that allow machines to learn from data and improve with

experience.

• Deep Learning: A more advanced approach to machine learning that uses

multi-layered articial neural networks, allowing machines to process and

learn from large volumes of unstructured data, such as images and text.

Understanding the history and dierent types of articial intelligence is

essential to recognize their inuence on educational management, where the

potential of these technologies can be both transformative and limiting.

1.3 Applications of articial intelligence in educational

management

The implementation of articial intelligence (AI) in educational

management has generated a signicant impact in various areas, facilitating

administrative processes and improving the learning experience of students.

1.3.1 Administrative automation

Among the most prominent applications of AI in educational management

is the automation of administrative tasks. Educational institutions face a huge

burden related to data management, planning, student registration, and

performance evaluation. Articial intelligence can help simplify and streamline

these processes so that faculty and sta can spend more time teaching and

interacting with students. The main features oered by administrative

automation using articial intelligence include:

• Data governance: AI-based education management platforms can collect,

process, and analyze large volumes of student data, making it easier to

make informed decisions. This includes managing academic records,

aendance, and test scores.

• Schedule planning: AI-powered tools can help you craft class schedules

more eciently, considering various variables such as teacher availability,

course demand, and student preferences.

• Registrations and enrolments: AI systems can manage the enrolment

process, making use of chatbots that assist students in real-time,

answering questions and guiding them through the steps required to

complete their enrolment.

• Educational evaluation and planning: AI can automate the evaluation of

exams and papers, using algorithms that provide accurate feedback and

allow students to know their performance immediately, fostering a

culture of continuous improvement.

1.3.2 Characterization of learning

In the individualization of learning, the student has his or her own pace and style

of learning, and articial intelligence allows learning resources and methods to

be adapted to particular needs. Thanks to advanced algorithms, each student can

be provided with an educational experience more tailored to their specic needs.

Keyways AI enables personalized learning include:

• Learning data analysis: Through academic performance tracking, AI can

identify paerns and trends in student learning, allowing educators to

oer targeted interventions and recommendations tailored to each

student.

• Adaptive learning resources: AI-integrated online learning platforms can

adjust the content and diculty of lessons based on the student's progress.

If a student shows strengths in an area, the system can oer additional

implications; If you are struggling, support resources can be provided.

• Virtual learning assistants: This type of technology can help students

solve doubts in real time, oer guidance on study materials, and facilitate

a more interactive learning environment. This translates into an enriched

and more student-centered experience.

• Collaborative learning: AI can identify groups of students with similar

interests or skill levels and facilitate interactions between them, promoting

collaborative learning that initializes synergies and dynamism in the

classroom.

So, articial intelligence is transforming educational management, allowing

greater eciency in administrative processes and personalizing the learning

experience. However, it is essential to approach the implementation of these

technologies with an ethical and responsible approach, ensuring that their use

benets all actors in the education system.

1.4 Ethical implications of articial intelligence in education

The integration of articial intelligence (AI) in the education sector has

brought a number of benets, including process automation, personalization of

learning, and increased administrative eciency. It also raises important ethical

questions that need to be addressed to ensure responsible and equitable

implementation (Kamalov et al., 2023). Among these issues, the two most

pressing are privacy and data protection, as well as transparency and algorithmic

bias.

The automated collection and analysis of personal data are fundamental

elements of AI's work. In the educational context, this means collecting

information about students, faculty, and administrators, including academic

data, achievements, behavior, and interests. While this information can be used

to improve teaching and learning, it also poses signicant risks.

• Informed consent: The backbone of privacy is ensuring that individuals

understand what data is being collected and for what purposes. It is

critical that an informed consent system is implemented that allows

students and parents to decide if they want their personal information to

be used by AI systems.

• Data storage and handling: The way personal data is stored and handled

must be secure and comply with legal regulations on data protection. A

breach in security can lead to leaks that expose sensitive information,

aecting users' trust in the education system.

• Right to be forgoen: In the digital environment, students have the right

to request the deletion of their data once it is no longer needed. However,

this right is often not eectively recognized on platforms that use AI,

which can lead to the perpetuation of negative information about a

student, aecting their academic and professional future.

1.5 Transparency and algorithmic bias

For innovation in education, the important ethical issue is the need to

guarantee the transparency of the algorithms used in the sector. AI-driven

decisions, such as admissions recommendations, academic diagnoses, and

performance evaluations, are often based on complex models that teachers and

students don't easily understand.

• Algorithmic bias: The risks associated with a lack of transparency is the

potential for algorithmic bias, algorithms developed and trained using

historical data can reect the inequalities and biases that exist in society.

If these biases are not identied and corrected, the use of AI could

perpetuate discrimination, negatively aecting vulnerable groups such as

students from disadvantaged communities or ethnic minorities. It is

critical for educational AI developers to strive to create fair and

representative models, eliminating errors that may be present in training

data.

• Explainability: A lack of transparency also harms educators' ability to

understand and trust the decisions the AI system makes. Explainability is

essential for teachers and students to question and understand the

recommendations oered to them and can generate resistance and distrust

towards the new technologies implemented in learning spaces.

AI has the potential to revolutionize education, it is critical to address its

ethical implications. Protecting data privacy and combating algorithmic bias are

key to outlining a responsible and ethical path in the use of emerging

technologies in education (Chan, 2023). The educational community, together

with developers and senior managers, must work to establish clear standards

that guarantee equitable and accessible education for all.

1.6 Social responsibility in the implementation of AI in education

Social responsibility is a concept that has become increasingly important in

educational management, especially in the context of technology and its

applications in the teaching-learning binomial. The introduction of articial

intelligence (AI) in the education sector not only involves the use of advanced

technological tools, but also entails a number of rights and responsibilities that

must be considered to ensure a positive impact dedicated to learning and

teaching.

Incorporating AI into the education system can bring many benets, such as

personalizing learning and simplifying administrative processes. In contrast, this

transformation also creates ethical and social problems. Therefore, it is important

for educational institutions and technology developers to adhere to social

responsibility principles to ensure that the use of these tools benets all

participants involved in the educational process:

• Accessibility: A vital aspect of social responsibility is ensuring that access

to AI-based technologies is not limited to certain populations. It is crucial

to develop solutions that are inclusive and that consider the dierent

socioeconomic realities of students. The digital divide must be addressed

so that all students, regardless of their context, can benet from intelligent

learning.

• Fairness: The algorithms used in AI can perpetuate existing biases if not

implemented fairly. Social responsibility involves making a conscious

eort to identify and mitigate biases in AI systems that could harm certain

groups. For example, it's essential that personalized learning platforms

don't inadvertently favor students from certain demographics to the

detriment of others.

• Accountability in decision-making: The implementation of AI in

education can lead to automated decisions in processes such as admission,

performance evaluation, and resource allocation. It is vital that institutions

maintain a human approach to these decisions and not rely entirely on

algorithmic processes. Social responsibility involves the creation of

mechanisms that allow for human review and control in automated

decision-making.

• Transparency and accountability: Trust in AI is built on the transparency

of its processes. Institutions must clearly communicate how data and

algorithms are used to make decisions that aect students. In addition, an

accountability framework must be established that allows students and

educators to question and appeal the decisions made by automated

systems.

• Training and continuing education: To ensure the responsible use of AI

in education, it is essential that both educators and students receive

adequate training on the use of these technologies. This not only includes

knowing how to interact with AI-based tools, but also understanding their

ethical and social implications. Education must go beyond simple

technological use and encourage critical thinking about AI.

1.6.1 Towards a more responsible future

Social responsibility in the implementation of AI in education must be a

priority for all parties involved and requires a real commitment to address the

ethical and social concerns that arise with its use. Educational institutions,

technology developers, and policymakers must work together to create an

environment where AI not only improves educational eciency, but also

promotes social justice and human development.

Therefore, the implementation of articial intelligence in education entails

a series of social responsibilities that must be actively managed. Only through an

ethical and equitable approach can the transformative potential of AI be

maximized, ensuring that all students have the opportunity to reach their full

potential in an inclusive and fair learning environment.

Articial intelligence (AI) in education management not only entails a

number of challenges that must be addressed seriously and responsibly, but also

provides signicant opportunities to transform our educational practices and

improve student learning. With the continued advancement of technology, it is

imperative that educators, administrators, and education policymakers

collaborate to maximize the benets of AI while minimizing its risks. Among the

main challenges facing the implementation of AI in education is the digital divide

in synergy with technological advances. On the other hand, there are many

educational institutions, especially in rural or disadvantaged contexts, which lack

adequate access to devices and internet connection. This inequality can

exacerbate dierences in educational opportunities and limit the eective use of

AI.

The issue of data privacy and security is another major concern. The bulk

collection of students' personal data raises questions about how this data is

managed and protected. Institutions must ensure that they comply with data

protection regulations, and that data is not used in an inappropriate or

exploitative manner.

In addition, there is a risk of algorithmic bias. AI systems are fed by

historical data, and if this contains biases, AI can perpetuate and amplify these

biases in its decisions. This is especially concerning in education, where a bias in

student assessment can lead to wrong decisions about their potential and

opportunities. Resistance to change among educators and administrators can

also be a signicant obstacle. The introduction of new technologies usually faces

skepticism and lack of adequate training. The need to develop digital skills in

education sta is crucial for the successful integration of AI into teaching and

learning processes.

Despite this panorama, the opportunities presented by AI in educational

management are promising and the personalization of learning is one of the most

prominent applications of AI. Through the use of data analysis and adaptive

algorithms, AI systems can create personalized educational experiences that

adjust to the needs and learning rhythms of each student, generating a school

climate adapted to the real context of the members in the classrooms.

This is how automating administrative tasks can maximize the time for

educators to focus on teaching and personal interaction with students. AI can

take care of repetitive tasks such as exam correction, aendance management,

and schedule planning, thus allowing education sta to spend more time

developing innovative teaching strategies (Chiawa, 2023). AI can facilitate the

analysis of school performance and the early identication of at-risk students;

And this is possible by using analytical tools, institutions can beer predict

student achievement and develop proactive interventions that help students stay

on track for educational success.

So, continuous training that can be oered through AI-powered platforms

is a great opportunity to improve teacher education. Allowing access to online

courses and resources not only improves digital skills, but also fosters a culture

of continuous learning that is vital in today's fast-paced technological world.

With an ethical and responsible approach, articial intelligence has the potential

to transform the educational eld in ways that benet students and teachers

alike.

The incorporation of articial intelligence in educational management

represents a signicant step towards the modernization and eciency of the

education system. It also raises important ethical and social considerations that

need to be carefully addressed. As educational institutions begin to integrate AI

tools, it is essential to reect on the impacts and implications of their use in

learning, teaching, and administration.

In this sense, the characterization of learning through AI can transform the

educational experience of students, allowing an adaptive approach that adjusts

to individual needs. In other words, AI-based platforms can analyze students'

academic performance, identify their strengths and weaknesses, and oer

specic resources and materials to help them integrate multiple intelligences.

However, this personalization must be carefully designed to avoid creating

systems that perpetuate existing inequalities or ignore the needs of minority

groups. In turn, it is crucial that the decisions made by machines are transparent

and understandable for students, parents, and educators.

While AI can improve operational eciency and allow educators to focus

on more creative and pedagogical aspects of their profession, it is also critical to

consider the implications of the dehumanization of education. The absence of

human interaction in certain processes could aect the quality of the educational

experience, as the emotional and social context of learning is equally important.

Emphasis should be placed on privacy and data protection as a fundamental

maer when using articial intelligence systems that collect and analyze personal

information. Institutions should ensure that appropriate measures are in place to

protect this data and that students and their families are informed about how

their information will be used. Therefore, it is vital to establish clear and adequate

policies for the management of information.

Regarding algorithmic bias, it is essential to recognize that AI systems are

existing social and racial inequalities. The implementation of articial

intelligence in education must be constantly reviewed and audited to minimize

these biases and ensure that all students are treated equally. The responsibility

for monitoring these processes lies not only with technology developers, but also

with educational administrators and legislators who establish regulations.

It is important that the adoption of these new technologies is carried out

with a focus on social and ethical well-being. Educational institutions must

become leaders in the discussion on the ethics of articial intelligence and its

application in learning. Considering the voices of students, parents, and

educators is essential to creating a framework that not only seeks to improve

educational eectiveness, but also to promote equity and social justice (Akgun,

& Greenhow, 2022). Therefore, articial intelligence has great potential to

transform educational management, but its implementation must be guided by

solid ethical principles and relevant social responsibility. Only in this way can it

be guaranteed that its use contributes to the integral development of students

and to the construction of a fairer and more accessible education for all.

1.7 Ethics and deontology of articial intelligence in the education

sector

The main ethical issues that arise when implementing articial intelligence in

education is equitable access, as not all students have the same level of access to

technology, which can lead to dierences in learning outcomes. This raises

questions about social justice and the need to adopt inclusive policies that ensure

that all students, regardless of their socioeconomic background, have equal

access to the benets that AI brings. In addition, another important aspect to

consider is the bias of the algorithm. Articial intelligence systems can perpetuate

or even amplify existing errors in the data they are trained on. Therefore, if an

algorithm is trained on data that reects racial or gender inequalities, the system

can reproduce similar errors in its recommendations or rankings, which can lead

to unfair and discriminatory results. This requires the development of

transparent and veriable algorithms that allow teachers and administrators to

identify and eliminate any potential bias.

Privacy and data protection are also important ethical issues when

applying articial intelligence to education. The handling of sensitive student

data, such as grades and behavior, is subject to strict privacy regulations. Data

collection should be ethical and transparent and provide parents and students

with information about how their data will be used.

1.7.1 Social and Cultural Eects

The incorporation of articial intelligence into education not only has

ethical implications but also important social and cultural implications. The

personalization of learning is an eect that worries many educators and parents

because it plays a more active role in learning and there is a risk of weakening

the human connection between students and teachers. Education is not only

about imparting knowledge, but about building interpersonal relationships,

empathy and developing social skills. Education is not a homogeneous

phenomenon and AI tools must be developed considering the cultural and

contextual characteristics of each learning community.

This requires a localized approach to creating AI-based educational

software that considers cultural diversity and dierent student needs. Finally, the

use of articial intelligence could also inuence the public's expectations of

education. An increased reliance on technology can lead to the misconception

that the quality of education can only be measured through quantitative data.

This approach may underestimate core values such as critical learning, creativity,

and problem-solving, which are dicult to measure but are equally important

for students' well-rounded development.

1.7.2 Deontology and Guiding Principles

Deontology in the context of articial intelligence (AI) applied to

education is a crucial aspect that denes the ethical conduct of developers and

the institutions that create and apply these technologies. Understanding the

behavioral norms that professionals in this eld must follow is essential to ensure

that AI benets all actors involved in the educational process.

A well-dened code of conduct is critical for developers of AI technologies

in the education sector. This code should address key aspects such as:

• Integrity: Developers should strive to create systems that are fair and don't

perpetuate bias. A commitment to scientic and technical integrity is essential to

building algorithms that operate ethically.

• Transparency: AI designers should adopt practices that favor

transparency in algorithmic processes. This involves not only documenting how

models work, but also clearly communicating to educators and students how AI-

based decisions are made.

• Privacy: Protecting students' personal data is an essential principle that

should guide the development of AI tools. Developers must ensure that

regulations such as the General Data Protection Regulation (GDPR) in Europe, as

well as other related legal frameworks, are complied with.

• Collaboration: Fostering interdisciplinary collaboration between

developers, educators, and psychologists is key to creating eective tools. This

not only improves AI applications, but also helps identify and mitigate potential

ethical risks.

A code of conduct that urges developers to act responsibly and ethically

can contribute signicantly to creating an educational ecosystem in which

technology is at the service of learning and human development.

1.7.3 Responsibility and Accountability

Responsibility and accountability are fundamental concepts that must be

present in any AI project aimed at the educational eld. Lack of accountability

can lead to problematic situations that negatively aect students and the system

as a whole (Zhai et al., 2024). Some things to consider include:

• Identication of Responsible Parties: It is essential that clear roles are

established in the development and deployment of AI. This includes identifying

those responsible for the design, implementation, and oversight of the

technologies used. Those who design and apply AI must be responsible for their

decisions and actions.

• Impact Assessment: The implementation of AI tools should be preceded

by ethical impact assessments that analyze how they will aect various

educational communities. These assessments help to anticipate and minimize

harmful consequences before technologies are deployed.

• Complaint Channels: Educational institutions should establish accessible

channels for users, including students and teachers, to report issues related to the

use of AI. Feedback systems are essential for detecting and correcting errors or

biases in the operation of AI.

• Ongoing Training: Both developers and educators should receive ongoing

training on ethical aspects related to AI. This helps to ensure that all parties are

aligned around the principles of ethics and deontology, promoting a responsible

use of educational technologies.

The combination of a strong code of conduct and a strong commitment to

responsibility and accountability will enable progress towards an ethical,

responsible and win-win integration of AI in the education sector.

1.7.4 Ethical Risks in the Use of AI

The use of articial intelligence (AI) in the education sector has

transformed the way students learn and educators teach. Educational platforms

that use AI often collect comprehensive information to personalize the learning

experience, which involves considerable risk in terms of privacy. The data

collected may include:

• Academic information and student performance.

• Learning behaviors and paerns.

• Demographics (age, gender, geographic location).

Improper handling of this information can have devastating

consequences. From the sale of data to third parties without consent to

unauthorized access by malicious entities, the possibilities are worrying. The

exposure of sensitive data can result in irreparable damage to students'

reputations, as well as their trust in educational institutions.

In addition, the lack of clear and robust regulations on data protection in

many jurisdictions increases the vulnerability of these platforms and, therefore,

of their users. Educational institutions must adopt rigorous and transparent

privacy policies, ensuring that data is used only for the purposes for which it was

collected. Implementing security measures, such as data encryption and

restricted access, is crucial to mitigating these risks.

AI algorithms are designed to make predictions and decisions based on

large volumes of data. But, if the training data contains bias or is representative

of a particular population, the results may be unfair and discriminatory. In the

educational context, an algorithm that assesses a student's potential may favor

certain demographics to the detriment of others. This can manifest itself in:

• Unequal access to educational resources.

• Biased evaluations and ratings.

• Inequality in personalized learning opportunities.

This bias can reinforce existing stereotypes and perpetuate inequities in

the education system. Students who are disadvantaged, whether because of their

socioeconomic background, race, or gender, are at risk of receiving a lower-

quality education due to biased algorithmic decisions.

To address these issues, it is essential for AI developers to work on creating

transparent and auditable algorithms, which are capable of identifying and

correcting biases in their data models. In addition, diversity in development

teams should be encouraged to ensure that multiple perspectives are present in

the design and implementation of AI-based educational tools (Koçak et al., 2024).

Therefore, articial intelligence has the potential to revolutionize education, the

associated ethical risks, such as data privacy and algorithmic bias, must be

managed seriously. Educational institutions, together with developers of AI

technologies, have a responsibility to establish practices that ensure ethical and

fair use of these tools, protecting all actors involved in the educational process.

1.8 Good Practices and Mitigation Strategies

The integration of articial intelligence (AI) in the education sector

presents several opportunities to improve teaching and learning. Therefore, it is

essential to implement good practices and mitigation strategies that ensure a

responsible and equitable use of these technologies. Transparency in the use of

algorithms is essential to ensure trust in AI tools applied to education. This means

that both developers and educational institutions need to be clear about how

algorithms work and what criteria they use to arrive at specic decisions. Among

the strategies that can be adopted, we highlight:

• Clear documentation: Provide manuals and appropriate documentation

that explain the algorithms and decision-making process. This material should

be accessible not only to educators and administrators, but also to students and

their families.

• Audits and reviews: Conduct independent audits and regular reviews of

algorithms and their results. This will allow you to identify biases or errors and

adjust the models as needed. The results of these audits should be published to

promote accountability.

• Education on the use of AI: Include in academic programs content on how

AI works, its benets and limitations. This will help both educators and students

have a deeper and more critical understanding of the tools they use.

• Active communication: Establish communication channels where

concerns and suggestions related to the use of AI can be shared. Not only does

this help solve problems in real-time, but it also strengthens collaboration

between all stakeholders.

1.8.1 Involvement of the Educational Community

The involvement of the educational community is another essential

strategy to mitigate the ethical risks of AI in education. This means that all actors

involved (students, parents, educators, and administrators) must have an active

and signicant role in the process of implementing and evaluating these

technologies. Some best practices in this area are:

• Fostering participation: Creating forums and spaces for dialogue where

the impacts and uses of AI in the educational environment are discussed. The

active participation of students and parents can provide valuable perspectives

that inuence decision-making.

• Interdisciplinary collaboration: Foster collaboration between dierent

disciplines and areas of knowledge to address the ethical conicts of AI from

multiple approaches. This may include the participation of experts in ethics, law,

and technology.

• Training programs: Oer training programs to all levels, from educators

to students, on the responsible and ethical use of AI. This will ensure that

everyone is empowered to address potential issues and contribute to a safe

educational environment.

• Continuous evaluation: Implement feedback mechanisms that allow the

use of AI and its impact on learning to be constantly evaluated. This should

include surveys and focus groups to gather feedback and experiences on their

use in the classroom.

Transparency in the use of algorithms and the involvement of the

educational community are essential to mitigate the ethical risks associated with

articial intelligence in education. These practices not only foster trust in

emerging technologies, but also contribute to a more equitable and responsible

educational environment.

The application of articial intelligence (AI) in the education sector has

generated growing interest in the academic community, as well as in institutions

seeking to improve the quality of learning. Through various case studies,

successful implementations and lessons learned can be identied that can serve

as a guide for the future use of AI in education.

1.8.2 Lessons Learned and Future of AI

The most common mistakes in AI applications in education is the lack of

prior training of teachers, they do not receive the necessary training to eectively

use these tools, which limits their potential. It is essential that any AI

implementation includes accessible and eective training programs for teachers.

Ethics also play a fundamental role. In some cases, it has been observed

that algorithms can perpetuate biases or discriminate against certain groups of

students (Chen, 2023). During the implementation of an automated scoring

system for entrance exams, a university in the United States had to face criticism

for obtaining results that favored students from privileged backgrounds. This

experience underscores the need to develop algorithms that are fair and

representative.

It is vital to adopt a collaborative stance between students, educators and

technology developers. The active participation of all stakeholders ensures that

AI solutions are not only focused on the technology, but also on the real needs of

the educational process. In addition, the development of a data protection policy

is indispensable. Proper management of student data is crucial to maintaining

their privacy and trust.

The future of articial intelligence (AI) in education promises to be

transformative and revolutionary, oering new opportunities to improve the

quality of learning and teaching. While this development is not exempt from

ethical and deontological boasts, they must be addressed to ensure that the

implementation of AI benets all actors involved in the educational process. By

analysing data collected on students, articial intelligence can help tailor

curricula to each student's individual needs, facilitating a more inclusive

education. This translates into the possibility for each student to progress at their

own pace, receiving support according to their level of understanding and skills

(Ellikkal & Rajamohan, 2024).

In addition, education itself must adapt to the presence of articial

intelligence in the classroom. It is essential that content on digital literacy and AI

ethics be integrated into educational programs to prepare future citizens to

interact with these tools critically and responsibly. Students must be able to

understand not only how AI works, but also the ethical implications of its use,

including algorithmic decision-making and the social impact of technology.

However, the implementation of AI in education faces several setbacks

and risks. Among them are:

• Inequalities in access: Not all students and educators have the same access

to technology. This can widen the education gap rather than narrow it.

• Data management: Data collection and analysis must be carried out

ethically and transparently to protect student privacy.

• Dehumanization of learning: Relying too much on AI can lead to an

education that relies too heavily on algorithms, to the detriment of human

interaction and the development of social skills.

To address these challenges, it is crucial that there is a collaborative

approach to the implementation of AI in education. Educational institutions,

technology developers, policymakers, and the community at large must work

together to establish good practices and policies that regulate their use.

1.8.3 Towards a hybrid model

The creation of a hybrid educational model where AI complements and

enhances the work of the teacher, helping to create more dynamic and interactive

learning spaces. Educators could use AI tools to assess their students’ progress

and design more impactful teaching strategies more eectively. It is through the

use of AI-powered learning management systems that teachers can receive alerts

about students who might need additional aention, allowing for early

intervention.

Likewise, the continuous training of teachers in the use of AI tools is

essential. They must be prepared to integrate these technologies in a way that

fosters a nurturing and ethical learning environment. This involves not only

being procient in the use of technology, but also understanding its ethical

implications and its impact on teaching and learning. The future of AI in

education is full of promise and opportunity, but also signicant contrasts that

require an ethical and responsible approach. The key will be to nd a balance

that allows us to move towards a more personalized and inclusive education,

without losing sight of the importance of human interaction and respect for

fundamental ethical values. How society addresses these issues will determine

the real impact of articial intelligence on our educational institutions and, by

extension, on future generations.

The integration of articial intelligence (AI) in the education sector poses

a series of challenges and opportunities that must be addressed from an ethical

and deontological perspective. As technologies advance and become more

ubiquitous, their impact on learning environments becomes increasingly

palpable, presenting both benets and risks that need to be carefully weighed.

It is essential to recognize that AI has the potential to revolutionize

education by personalizing the learning experience. Tools such as virtual tutors,

adaptive learning platforms, and learning management systems can facilitate a

more student-centered approach. However, with this potential comes a

responsibility to ensure that the data used to power these systems is managed

ethically. The collection, storage, and processing of students' personal

information must be governed by clear principles that prioritize security and

privacy.

Also, algorithmic bias is a signicant factor in the educational context,

algorithms used in AI tools can perpetuate or even amplify existing inequalities

if not designed carefully. For example, if the training data used to create these

algorithms reects historical or cultural biases, the results produced by the AI are

likely to show bias as well. Not only does this aect equity in education, but it

can also have repercussions on the self-esteem and future opportunities of

vulnerable students. Therefore, it is crucial to implement auditing and review

mechanisms for algorithms used in educational applications to ensure that they

are fair and equitable.

Education must be accompanied by training that prepares teachers to

integrate AI into their teaching methods without losing sight of the humanity and

critical judgment that are fundamental in the educational process. Educators

must be empowered to question and evaluate the analysis oered by AI tools,

integrating their professional expertise with technological capabilities (Seo et al.,

2024).

Based on this, the need arises to build a regulatory framework that

guarantees ethics in the use of AI in education. This framework should be based

on principles that promote transparency, accountability, and the participation of

diverse stakeholders, including students, parents, educators, and technology

developers. Only in this way can we aspire to create an educational environment

where technology complements and enhances teaching, rather than replacing the

human interaction that is essential for eective learning.

It is vital to foster an ongoing dialogue about the ethical and deontological

implications of articial intelligence in education, as technology continues to

evolve. Ongoing research, as well as the exchange of experiences and good

practices between institutions, can contribute to a deeper understanding of how

AI can be used to improve education without compromising ethical principles.

Articial intelligence has the potential to transform education, but its

implementation must be guided by a serious commitment to ethics and

deontology. It is a eld in which it is crucial not only to seek technological

innovation, but also well-being and equity for all actors involved in the

educational process. With a proactive and thoughtful approach, it is possible to

achieve a positive and responsible integration of AI in education.

Chapter II

Teaching and learning with articial intelligence

applications

The integration of articial intelligence (AI) in the education sector opens

up several opportunities to improve teaching and learning. Therefore, it is

important to implement best practices and mitigation strategies to ensure the

responsible and equitable use of these technologies. In this chapter we consider

two important aspects: transparency in the use of algorithms and the

commitment of the educational community.

Transparency in the use of algorithms is essential to ensure trust in AI tools

used in education. This means that both developers and educational institutions

must have a clear understanding of how algorithms work and the criteria they

use to make specic decisions. Among the possible strategies we can distinguish:

2.1 Augmented and virtual reality, applications in education

Augmented reality (AR) and virtual reality (VR) represent an additional

dimension in educational innovation facilitated by articial intelligence. These

technologies transform the classroom into an immersive environment, allowing

students to interact with concepts in tangible and visual ways (Christopoulos et

al., 2018). Among the applications in education we have:

- Laboratory simulations where students can experiment without the risks

associated with real materials.

- Virtual eld trips to historical sites, museums, and science spaces that

would otherwise be inaccessible.

- Educational games that encourage teamwork and the development of

social skills in controlled environments.

These immersive experiences not only enrich learning, but also encourage

students' curiosity and interest in complex topics, facilitating a beer

understanding and assimilation of the subjects.

Gamication is another innovative aspect where articial intelligence is

having a signicant impact. By integrating elements of play into the learning

process, it seeks to increase student engagement and participation. AI systems

can personalize gamication, adjusting diculty levels and makes it an eective

method for teaching, improving skills, and encouraging continuous learning.

The application of articial intelligence in education is not only enabling more

ecient management but is also transforming the very concept of learning.

Through a personalized approach, immersion in AR and VR environments, and

the integration of gamication, AI is creating a more inclusive, dynamic, and

eective educational future.

2.3 Ethics, deontology and considerations in the use of AI

The collection and processing of data through AI-based tools often

involves the handling of private student information, which raises serious

privacy and security concerns. Education systems use data to personalize

learning, identify areas for improvement, and tailor pedagogical interventions.

Among the main concerns are:

- Informed consent: It is essential that informed consent is secured from

students and their parents before any data is collected. This involves not

only obtaining authorization, but also ensuring that the parties involved

understand how their data will be used.

- Transparency in data use: Institutions must be transparent about how data

is collected, stored, and used. A lack of clarity can lead to mistrust among

parents and students.

- Information security: It is vital to implement robust security measures to

protect sensitive information. Security breaches in data handling can

result in the exposure of personal information, which would not only

aect individuals, but also the reputation of educational institutions.

In addition, the possibility of biases in the data used to train AI systems

should be considered. If the data collected is biased, AI can perpetuate or even

exacerbate existing inequalities in education.

2.3.1 Implications for teachers

- Teacher replacement: One of the most common fears is that AI could

replace human educators, which could dehumanize the educational

process. It is essential that AI is seen as a tool that complements the work

of teachers instead of replacing it.

- Technological dependence: Dependence on AI tools can lead to a decrease

in teachers' pedagogical skills and critical capacity. Training in the use of

these technologies must be accompanied by a strengthening of

interpersonal and teaching skills.

- Inequalities in access to technology: Not all educational institutions have

the same access to AI tools, which can lead to disparities in the quality of

education oered. Teachers need to be aware of these dierences and

work to ensure that all students have equal opportunities to benet from

AI.

- In summary, while articial intelligence has the potential to transform

education, it is critical that educational institutions address the ethical

aspects that its use entails. This involves ensuring data privacy and

preparing teachers to leverage technology responsibly and eectively,

thus ensuring an equitable and humane educational environment.

Articial intelligence (AI) is rapidly transforming the education landscape,

and its future promises to be even more impactful. As technology advances,

educational institutions, from basic education to universities, must adapt to these

innovations to remain relevant and eective (Kamalov et al., 2023). There are

several emerging trends that are shaping how AI is integrated into the education

sector:

- Adaptive learning: AI makes it possible to create personalized learning

experiences tailored to each student's individual needs. Through machine

learning algorithms, systems can analyze student performance in real-

time and adjust the content, diculty, and pace of learning according to

their specic requirements.

- Virtual assistants: Increasingly, institutions are using chatbots and virtual

assistants to provide support to students and teachers. These tools can

answer frequently asked questions, aid in problem-solving, and provide

educational resources, freeing up teaching sta to focus on more complex

and creative tasks.

- Automated assessment: AI facilitates the automatic correction and

assessment of exams, assignments, and projects, which not only saves

time, but also reduces human bias in grading. This allows teachers to focus

on qualitative feedback and improving teaching.

- Educational data analysis: The collection and analysis of data using AI

allows you to identify trends and paerns in student performance. This

data can help institutions beer understand students' diculties and

design more eective intervention strategies.

The future of articial intelligence in education is promising and full of

possibilities, including:

- Integration of augmented and virtual reality: The combination of AI with

augmented reality (AR) and virtual reality (VR) technologies is anticipated

to revolutionize the way we teach and learn. Students will be able to

immerse themselves in virtual environments where they can interact and

experience concepts in a practical and visual way, thus increasing their

understanding and retention.

- Global collaboration: AI will enable the creation of online educational

platforms that will connect students and educators around the world,

fostering collaborative learning. These platforms will be able to adapt

content and methods to dierent cultural contexts, enriching the

educational experience of all participants.

- Human-AI interaction: Future AI applications are designed to work

alongside teachers, complementing and expanding their educational

work. This partnership between humans and machines will not only

improve teaching but will also allow educators to focus more on creativity

and critical thinking, skills that are essential in today's world.

- 21st century skills development: AI will be able to help students develop

key skills such as problem-solving, collaboration, and adaptability. With

the use of personalized, AI-based learning platforms, learners will be

beer prepared to meet the challenges of an ever-changing world.

The transcendence of AI in education oers a horizon full of innovative

opportunities that, if properly implemented, can revolutionize the way we learn

and teach. Likewise, it is crucial to address these advances ethically and

responsibly to ensure that all students benet from this transformation and

represents a paradigmatic shift that not only transforms the educational

environment, but also redenes the role of the actors involved in the teaching-

learning process (Alam & Mohanty, 2023). Therefore, it is imperative that

educational institutions adopt and adapt new technologies to improve their

eectiveness and relevance in a global context.

2.3.2 Personalization of learning and eciency in management

AI also makes it possible to optimize the administrative management of

educational institutions, from automating administrative processes to eciently

managing resources, articial intelligence can free up valuable time for educators

to focus on what really maers: teaching. Smart tools can aid in resource

allocation, scheduling planning, and tracking student progression, leading to

greater operational eciency.

Incorporating advanced technology, such as AI, into the educational

curriculum not only prepares students for the future, but also fosters the

development of critical 21st-century skills. Competencies such as critical

thinking, problem-solving, and creativity are enhanced by using technological

tools that simulate real-world scenarios and allow students to experiment and

collaborate in innovative ways.

On the other hand, the application of articial intelligence in the eld of

education is not without incompatibilities. Ethical aspects related to data privacy,

equity in access to technology and bias in the algorithms used are issues of great

relevance. It is crucial that educational institutions address these issues

proactively, establishing clear policies on the use of data and promoting

transparency in the development of educational technologies.

Emerging trends, such as adaptive learning and the use of virtual

environments, are shaping a new horizon for education. AI has the potential to

revolutionize not only how it is taught, but also how it is learned, providing a

rich and varied educational experience that prepares students to face

technological changes.

Educational management and innovation through articial intelligence

applications are fundamental pillars for the education of the future. The key to

eective development lies in collaboration between all actors involved, ensuring

that technological tools include and benet all students, creating an inclusive

educational environment adapted to the needs of the 21st century.

2.4 Teaching and learning with articial intelligence applications

The implementation of articial intelligence (AI) in education has changed

the way we teach and learn. As technology advances, new opportunities open up

that allow the educational process to be optimized, making it more personalized,

accessible and ecient (Labadze et al., 2023). AI oers tools that analyze the

behavior and performance of students, allowing the creation of content adapted

to their individual abilities:

- Continuous Assessment: Using algorithms that collect data on students'

interactions with educational material, AI can identify areas where a

student is struggling and oer specic resources to address them. This

allows educators to tailor their teaching methods to each student's needs,

thus improving their understanding and retention of information.

- Resource Recommendations: AI-based educational platforms can suggest

study materials, exercises, and hands-on activities to students based on

their preferences and previous results. This not only optimizes study time,

but also motivates students to explore dierent learning modalities.

- Self-directed learning: Articial intelligence fosters student autonomy by

allowing them to access tools and resources independently. Not only does

this strengthen their ability to learn on their own, but it also teaches them

valuable skills that will serve them well throughout their lives.

AI also plays a crucial role in creating a more accessible and inclusive

educational environment. Smart systems can cater to the diversity of educational

needs, ensuring that all students, regardless of their abilities, have access to the

same quality of education:

- Support for Students with Disabilities: Tools such as reading software,

automatic captioning, and real-time translation make it easier for students

with hearing or visual disabilities to learn. Thanks to AI, adaptive

resources can be created that adjust to the abilities of each student,

reducing the barriers often encountered in a traditional academic

environment.

- Language and Communication: Articial intelligence makes it possible to

eliminate language barriers through translation applications. Students

from dierent linguistic backgrounds can access materials in their native

language, which enriches diversity and promotes collaboration between

students from dierent cultural backgrounds.

- Facilitation of Inclusion: Inclusive classrooms benet from AI tools that

allow interaction between students of diverse abilities. Through

collaborative online activities and adaptive exercises, a learning

environment is fostered where every voice is heard and valued.

In this sense, administrative and assessment tasks often consume a signicant

amount of time, which can take away from eective teaching:

- Automation of Administrative Tasks: AI tools are capable of managing

repetitive tasks, such as scheduling classes, managing aendance records,

and sending notications to students. This frees up valuable time for

educators to focus on lesson planning and direct interaction with students.

- Automated Correction and Evaluation: AI can facilitate the evaluation

process through systems that perform automatic corrections. From online

quizzes to essays, these systems analyze content and provide objective

grading in a maer of minutes. Not only does this reduce the teacher's

workload, but it also provides students with near-instant feedback.

- Data Analytics for Continuous Improvement: By using data analytics, AI

can help educators identify trends in student performance, as well as areas

for improvement in the course design itself. This data can be used to

continuously adjust and improve the educational oer, ensuring that

students' needs are met in the future.

Overall, articial intelligence is transforming teaching and learning in ways

that go beyond what could have been imagined a few years ago. The

personalization of learning, the improvement in accessibility and inclusion, as

well as the eciency and time savings it provides, are just a few examples of how

AI can be a powerful ally in the educational process. With the unication of these

technologies, the future of education promises to be more inclusive, eective and

enriching for each student, bringing us closer to a more equitable educational

model adapted to the demands of the 21st century.

2.4.1 Applications of Articial Intelligence in the Classroom

Assessment is a fundamental part of the educational process, and with the

help of AI, it can be done more eciently and objectively. Automated assessment

systems use algorithms to grade exams, essays, and other tasks, oering multiple

benets that can transform assessment in the classroom:

- Faster feedback: Automated systems can grade tests in seconds, allowing

students to receive near-instant feedback and correct errors in their

learning process.

- Standardization and objectivity in assessment: Unlike human assessment,

which can be subjective and inuenced by personal factors, AI provides a

more objective rating based on predetermined criteria.

- Performance analysis: These platforms can analyze student performance

over time, identifying paerns and areas where students may need

additional support.

The implementation of automated assessment systems allows teachers not

only to save time on exam correction, but also to gain a clear view of the group's

performance and adjust their teaching methods according to the needs identied

through the analytical data generated.

Simulation and augmented reality (AR) are immersive tools that are

revolutionizing the teaching of complex disciplines, such as medicine, science, or

engineering. Through the creation of immersive learning environments, these

technologies allow students to interact with content in a way that simply reading

or viewing could not achieve:

- Experiential learning: Simulations allow students to experience real-world

situations without the associated risks. For example, in a medical

simulation environment, students can practice surgical or diagnostic

procedures.

- Improved conceptual understanding: AR can overlay digital information

into the real world, helping students visualize abstract concepts. For

example, when studying chemistry, students can observe chemical

reactions in an interactive 3D environment.

- Motivation and engagement: These technologies make learning more

engaging and exciting, encouraging greater interest in course content and

promoting active participation.

Simulation and AR applications can also be used in areas such as history,

where students can experience historical events in a vivid and dynamic way. This

not only facilitates the retention of information, but also allows knowledge from

various disciplines to be integrated, making the learning process comprehensive

and enriching.

The use of articial intelligence in the classroom oers vast potential to

improve teaching and learning. Through virtual assistants, automated

assessment systems, and augmented reality simulations, educators and students

can benet from more personalized, ecient, and engaging teaching methods. It

is important to be aware of the limitations that this integration presents, as well

as the need for training and continuous support for both teachers and students

(Zouhri & Mallahi, 2024). In an increasingly digital world, AI is presented not

only as a tool, but as an indispensable ally in the evolution of education.

In this context, it is essential to analyze long-term projections and how AI will

transform the role of the teacher in the classroom. As AI technologies continue to

advance, new opportunities present themselves that can change the way we teach

and learn.

2.4.2 Long-Term Projections and the Evolution of the Teacher's Role

Long-term projections on AI in education suggest that its implementation

will become increasingly profound and multifaceted. As AI evolves, tools and

applications are expected to become more sophisticated, allowing the

educational experience to be personalized in ways that are just beginning to be

explored:

- Directed learning: One of the most exciting projections is the possibility of

oering highly personied learning experiences. AI systems will be able

to analyze each student's performance and learning styles, adapting

content and teaching strategies to meet individual needs. Not only will

this improve understanding of concepts, but it will also increase students'

motivation and engagement.

- More eective assessments: AI will enable more accurate and eective

assessments. Through advanced algorithms, applications will be able to

identify not only the student's performance in nal exams, but also their

areas of weakness and strength in real time. This information will allow

educators to adjust pedagogical strategies immediately, rather than

waiting until the end of an academic cycle to make changes.

- Lifelong education: With the rapid evolution of the labour market,

continuing education will be essential. AI will facilitate lifelong learning

by oering courses and resources tailored to the changing needs of

professionals. This includes exible training programs that t individuals'

schedules and preferences, making education more accessible to

everyone.

- Improved inclusivity: AI has the potential to improve accessibility in

education. Tools such as voice assistants and translation apps can help

overcome language and disability barriers. In this way, students from

dierent backgrounds and abilities will have the same learning

opportunities.

- Simulations and immersive learning: As augmented and virtual reality

technologies integrate with AI; teaching methods will become more

immersive. Students could participate in complex simulations that allow

them to experience real-world situations in a safe and controlled

environment, thereby improving their understanding and practical skills.

The introduction of articial intelligence in education also raises questions

about the future of the teacher's role. As automated tools take on certain

functions, it is crucial to consider how the teaching profession will be

transformed:

- Guides and mentors: With the availability of AI systems that can provide

immediate feedback, the teacher's role could shift more towards being a

guide and a mentor. Rather than being the sole source of knowledge,

educators will focus on facilitating learning, helping students navigate

their personalized learning experiences, and fostering critical thinking

and collaboration.

- Facilitators of the learning culture: Teachers will have a key role in creating

a positive learning environment. By leveraging AI to automate

administrative and assessment tasks, educators will be able to spend more

time building relationships with students and cultivating a learning

culture that values curiosity and personal development.

- Collaborators in technology implementation: As more AI tools are

integrated into the classroom, teachers will need to become experts in

these technologies. They will need to know how to use these tools

eectively and ethically and how to approach them in pedagogical terms.

This will require continuous and adaptive training.

- Ongoing Professional Development: Being an educator in an ever-

changing environment will require a commitment to continuous

professional learning. Teachers will need to keep up with new

technologies, methodologies and pedagogical approaches in order to

make the most of the opportunities oered by AI.

- Ethics and responsibility: With the integration of AI also come important

ethical considerations. Educators will need to address how student data is

used, ensuring that privacy and security are respected. At the same time,

they will need to instruct students about the responsible use of technology

in an increasingly digital world.

In general, the role of the teacher will evolve, becoming a guide and mentor

who fosters an inclusive learning environment adapted to the needs of each

student. To meet these changes, it will be crucial for educators to be well prepared

and commied to their professional and ethical development in an ever-evolving

world. The incorporation of articial intelligence (AI) into teaching and learning

has brought about a signicant change in the way education is approached. This

technological paradigm, although it has brought with it endless benets, also

presents contradictions that must be considered to maximize its potential in the

educational eld (George & Wooden, 2023).

AI allows educational content to be adapted to the needs and learning

rhythms of each student. Through advanced algorithms and data analysis, areas

of strength and weakness can be identied, thus oering materials and activities

that are tailored to individual capabilities. Not only does this improve learning

eectiveness, but it also increases student motivation and engagement.

AI-powered technology gives educators and students access to a vast array of

online resources, from learning platforms to specic apps that make it easy to

acquire new knowledge. This access democratizes education, allowing more

people, regardless of their geographical location or economic situation, to benet

from quality education.

AI can automate numerous administrative tasks, from managing enrollments

to evaluating exams. This allows educators to spend more time on eective

teaching and accompanying students, rather than focusing on bureaucratic tasks.

By optimizing these processes, school organization is improved and the

workload for teachers is reduced.

2.4.3 AI scenario in the sociocultural context

- Inequalities in Access to Technology: Despite eorts to democratize access to

education, there is still a signicant digital divide that aects many students.

Those who lack proper devices or a reliable internet connection may be left at a

disadvantage, preventing them from beneting from AI-powered tools.

- Technology Dependency: There is a risk that both educators and students will

become overly reliant on technology and AI tools, neglecting fundamental skills

such as critical thinking, creativity, and problem-solving skills without the

support of technology. Education must balance technological integration with

the development of essential skills.

- Ethical and Privacy Issues: The use of AI in education raises critical questions

about data privacy and the ethics of data handling. It is crucial that clear and

strict regulations are put in place to protect students' personal information and

ensure that technology is used responsibly and transparently.

- Resistance to Change: The implementation of AI-based educational practices

may encounter resistance from educators and administrators who are

accustomed to traditional methods. Adequate training and support needs to be

provided to ease the transition and help educators adapt to new tools and

approaches.

The impact of articial intelligence on education is being dened as we

continue to explore its potential to transform education management. While the

benets are indisputable, it is essential to approach technological paradigms

seriously and proactively. The key to a successful educational future lies in

nding a balance between incorporating advanced technologies and preserving

the essential values of education. The intersection of humanities and technology

can enrich the educational experience. Fostering interdisciplinary projects that

use AI while exploring ethical, social, and cultural aspects can prepare students

for a world increasingly inuenced by technology (Bahroun et al., 2023).

Education should not be limited to the transmission of knowledge or the

execution of automated tasks. An integrated approach should be taken that

fosters not only academic learning, but also students' emotional and social

development. Integrating AI into a framework that prioritizes the holistic well-

being of the student will be essential.

So, articial intelligence has the potential to transform education in

profound ways. It is the responsibility of all those involved in the educational

process – politicians, educators, parents and students – to work together to

maximize the benets. Only in this way can we ensure that the education of the

future is fair, inclusive and enriching for all.

Chapter III

Research in education with articial intelligence:

Interactive dialogic learning

Education is a fundamental pillar in the development of modern societies,

and educational research plays a crucial role in the improvement and evolution

of teaching and learning methods. As technology advances, the intersection

between education and articial intelligence (AI) has become increasingly

relevant, oering new opportunities to personalize the educational experience

and optimize the learning process. The authors explore the relationship between

educational research and articial intelligence, focusing on the current context

and the importance of AI in this eld.

The purpose of educational research is to generate knowledge that allows

improving teaching and learning processes. Since its inception, it has focused on

aspects such as pedagogy, didactics and the psychology of learning.

Traditionally, educational researchers have used qualitative and quantitative

methods to collect data and analyze it with the goal of understanding how

students learn, which teaching strategies are most eective, and how more

inclusive and accessible learning environments can be designed (Renjith et al.,

2021).

Today, educational research is facing new challenges and opportunities,

driven by globalization, digitalization, and technological advancement.

Traditional educational models are no longer sucient to address the diversity

of student needs and the rapid evolution of knowledge. It is therefore essential

that researchers adopt innovative approaches that integrate technological tools

that facilitate data collection and analysis, while promoting eective pedagogical

practices.

Articial intelligence is emerging as one of these innovative tools,

transforming the way educational research is conducted. AI techniques, such as

machine learning and natural language processing, allow researchers to analyze

large volumes of data more eciently and eectively, leading to more accurate

and useful ndings about learning and teaching.

Below are some of the reasons why articial intelligence is critical in the

context of educational research:

- Real-time data analysis: AI allows researchers and educators to analyze

data in real-time, providing a deeper understanding of how students are

performing in the classroom. By using AI algorithms, researchers can

detect paerns in student behavior and performance, facilitating quick

and eective interventions to address learning problems before they

become signicant obstacles.

- Eciency in data collection: Historically, data collection in educational

research has been a laborious process and often limited by time and

resource constraints. However, AI tools can automate much of this

process, allowing researchers to gain valuable insights more quickly and

accurately. This in turn promotes more agile and evidence-based research.

- Development of new pedagogical methods: The incorporation of AI in

education also stimulates innovation in pedagogical practice. Educators

can experiment with new teaching strategies and evaluate their

eectiveness using AI-powered tools, enabling an evidence-based

approach to the development of educational methodologies.

By addressing the limitations of traditional approaches and providing new

opportunities to personalize learning, AI is paving the way to a future where

education is more accessible, inclusive, and eective. As we continue to explore

the intersection of education and technology, it is critical that thorough research

is conducted that not only examines the benets of AI, but also addresses the

ethical and equity concerns associated with its implementation in educational

contexts.

3.1 Historical context of Articial Intelligence in Education

Since its inception, articial intelligence has sought to improve teaching

and learning, creating tools that can personalize education and meet the needs of

each student. The rst experiments in the eld of articial intelligence date back

to the 1950s. One of the pioneers in this eld was the mathematician and

computer scientist Alan Turing, who proposed that machines could think and

learn like humans. In retrospect, the use of articial intelligence in education

didn't emerge until the 1970s, when researchers began exploring the possibility

of creating systems that could adapt to student learning. One of the rst

educational programs to use the principles of articial intelligence was the

Intelligent Teaching System (ITS) program. These systems are designed to

provide students with personalized instruction, analyze their responses, and

tailor content to their needs.

Then in the 80s articial intelligence began to be integrated more widely

into educational practices, these programs allow evaluation and provide

interactive exercises so that students can practice and receive immediate

feedback on their performance. But these early eorts also encountered

problems. The limitations of computer technology at the time made it dicult to

develop more advanced systems and they were eectively adapted to dierent

learning styles.

3.1.1 Technological evolution

From the 90s onwards, the evolution of technology has profoundly

transformed the eld of articial intelligence in education. With the advent of the

Internet and the increase in data processing capacity, access to an unmatched

amount of information and educational resources was facilitated. This allowed

researchers to create more complex AI systems that could analyze large volumes

of data and continuously improve their eciency.

These techniques allow AI systems to not only perform specic tasks, but

also learn from the data they collect. In education, this has led to the creation of

adaptive learning platforms that personalize the learning experience for each

student, adjusting content and activities based on their performance and

preferences. The rise of mobile technologies and access to smart devices has also

played a crucial role in this evolution. Students can now access educational apps

that use AI to deliver interactive learning experiences anywhere, anytime. For

example, math and language apps that use chatbots allow students to practice

and receive instant assistance, making them more active in their learning process

(Grassini, 2023).

Also, data analysis has become an integral part of modern education.

Educational institutions use predictive analytics tools to monitor student

performance, identify paerns, and predict potential problems before they occur.

Not only does this help educators personalize their approach, but it also

contributes to the creation of more inclusive learning experiences.

From the rst tutoring systems to today's personalized learning platforms,

technology continues to transform the way we teach and learn. As we move into

the future, it is essential to continue to explore and take advantage of the

opportunities that articial intelligence oers to improve education and respond

to the needs of students in an ever-changing world. In this sense, from adaptive

learning platforms to educational data analysis, AI is impacting the way content

is taught and learned.

3.1.2 Adaptive learning platforms

These platforms use AI algorithms to analyze student performance in real-

time and adjust educational content according to their individual needs. This

allows for a personalization of learning, where each student progresses at their

own pace and according to their previous skills and knowledge.

Adaptive learning systems can:

- Identify areas of diculty and oer specic exercises to improve student

performance.

- Provide instant feedback and recommendations on additional resources

to facilitate learning.

- Analyze paerns of student behavior and achievement, allowing

educators to beer understand the needs of their students.

- Numerous studies have shown that this approach not only improves

content comprehension and retention, but also increases student

motivation by making the learning process more relevant and engaging.

These systems are designed to interact with students, providing real-time

help and support. Through chatbots and natural language processing

technologies, virtual assistants can answer questions, clarify concepts, and guide

students in their learning.

Educational institutions are collecting a wealth of data on student

performance, class engagement, and other key metrics. Using AI-powered data

analysis techniques, these institutions can extract valuable insights that allow

them to improve teaching and learning.

Applications of education data analytics include:

- Identifying trends in student performance, helping to spot issues before

they become academic crises.

- Segmentation of students according to their characteristics and needs,

which allows the development of proactive and personalized

interventions.

- Evaluating the impact of educational programs and methodologies,

informing future decisions about curriculum and other academic areas.

Today's applications of articial intelligence in education are reshaping

teaching and learning in innovative and eective ways. From platforms that

tailor learning to individual student needs, to virtual tutors that oer constant

support and data analysis that inform educational decisions, AI is becoming an

indispensable tool in education. Its integration into classrooms promises not only

to enrich the learning experience, but also to beer equip students to face the

transition from face-to-face learning to distance learning.

Through advanced algorithms and data analytics, adaptive learning

platforms can assess students' individual skills and needs, adjusting the content

and pace of learning based on their progress. This is especially valuable in

environments where students have dierent learning styles and developmental

paces. Some of the enhancements to personalized learning include:

- Adaptive content: Educational apps use AI to tailor learning resources to

each student's specic abilities. Not only does this help keep students

engaged, but it also maximizes learning eectiveness as students don't feel

overwhelmed or undervalued.

- Immediate feedback: Through continuous data collection, AI systems can

provide instant feedback on student performance. This allows learners to

identify areas for improvement quickly, facilitating a proactive approach

to their education.

- Diculty detection: AI can analyze paerns of behavior and performance,

identifying students who may be struggling with certain concepts. This

way, educators can intervene early and oer the necessary support. It is

critical to balance the use of technology with the human touch that

educators can provide (humanization of knowledge).

3.1.3 Ethical and privacy concerns and inequalities in technology

management

The use of AI in education also raises important ethical and privacy

questions. AI systems often require large amounts of personal data to optimize

their operation. This raises concerns about how this data is collected, stored, and

used. Some of the ethical concerns include:

- Student privacy: The collection of sensitive student data can expose

students to risk, especially if institutions do not implement adequate data

protection measures. Condentiality and information security must be

prioritized to maintain the trust of students and their families.

- Bias in algorithms: AI can perpetuate existing biases if not carefully

designed. If algorithms are trained on data that reects cultural or social

biases, this can result in unfair treatment of certain groups of students,

aecting their access and educational opportunities.

- Dehumanization of learning: There is a risk that over-reliance on AI in

education will lead to a less human learning experience. The relationship

between educators and students is a critical component that cannot be

replaced by technology. Therefore, it is vital to nd ways to integrate AI

in ways that complement, rather than replace, social and emotional

interaction in the classroom.

Despite the promises of AI in education, one of the most pressing concerns is

inequality in access to technology. Not all students and schools have the same

opportunities to benet from AI tools, which can lead to disparities in learning.

Some factors that contribute to this inequality include:

- Internet access: In many places, especially in rural and disadvantaged

communities, access to high-quality internet is still an issue. Without a

reliable connection, using online learning platforms and AI-based

resources becomes unfeasible.

- Training and training: Eective implementation of AI in education

requires not only hardware and software, but also training for educators.

Teachers must be prepared to integrate technology into their teaching

eciently, which is not always possible in contexts with limited resources.

Emerging technological innovations and new research methodologies oer a

fertile eld not only to improve the eectiveness of learning, but also to explore

how AI can leverage ethical and equitable aspects in educational institutions and

are revolutionizing the way education and educational research is conducted

(Walter, 2024). Some of the most promising trends include:

- Deep Learning and Neural Networks: These AI techniques are improving

the ability to personalize learning. As deep learning algorithms become

more sophisticated, they have the potential to analyze not only a student's

performance, but also their behavioral and emotional paerns during the

learning process. This allows for the creation of highly personalized

learning environments that are tailored to each student's individual needs.

- Augmented Reality (AR) and Virtual Reality (VR): These technologies

allow for the creation of immersive learning experiences. The combination

of AI with AR and VR can make it easier to simulate educational

environments that are inaccessible in real life. For example, in physician

training, students can practice surgical procedures in a virtual

environment where errors have no real consequences.

- Predictive Analytics: This technique is being used to anticipate student

performance and detect potential problems before they become signicant

obstacles. By leveraging large volumes of educational data, institutions

can identify factors that aect academic success and deliver timely

interventions.

- Recommendation Systems: Based on student preferences and behavior,

these systems can help guide students toward more relevant and

personalized educational resources. Not only does this increase the

eectiveness of learning, but it also motivates students to explore areas of

interest that they might not have considered.

3.2 Research methodologies with the application of articial

intelligence

The implementation of AI in educational research is also driving the

evolution of innovative methodologies that improve the validity and replicability

of studies in this eld:

- Mixed methodologies: The combination of qualitative and quantitative

approaches allows researchers to obtain a more complete view of

educational problems. Using AI tools to analyze large quantitative

datasets along with interviews and focus groups can oer unique insights

on how to improve teaching and learning.

- Data-driven research: With the increase in data collection through

learning platforms, researchers have access to unprecedented insights into

student behavior. By applying data mining techniques and statistical

analysis, they can uncover paerns and correlations that might go

unnoticed in traditional studies.

- Longitudinal studies: AI allows for long-term monitoring of students'

educational development, combining performance data with contextual

information from their family and social environment. This provides a

robust framework for understanding the inuence of multiple factors on

learning over time.

- Agile experimentation: The ability to quickly test dierent educational

approaches and measure their eectiveness through AI algorithms allows

for unprecedented adaptability in educational research. Real-time data

helps adjust strategies and provide immediate feedback to educators and

students.

In this context, future research in the eld of education with articial

intelligence is full of potential. Emerging technological innovations and new

research methodologies not only promise to improve educational practices, but

also oer the opportunity to address historical problems of inequity and access

in education. However, it is crucial that these opportunities are addressed with

due regard to ethics and privacy, ensuring that the integration of AI in education

is inclusive and benets all students. As we move forward, the education

community will need to collaborate to develop a robust framework that guides

the use of AI responsibly and sustainably.

After the COVID-19 pandemic, we have witnessed a signicant shift in how

educational research is conducted and how technology is applied in the

classroom. AI's ability to process large volumes of data, as well as its ability to

learn and adapt to various situations and learning styles, has opened up new

horizons for educators and students alike (Pantelimon et al., 2021).

3.3 Ethical Principles in Scientic Research

Scientic research is based on the search for knowledge and the generation

of information that can benet society. This search cannot be carried out without

considering the ethical principles that guide research practice. These principles

are fundamental to guarantee respect for the rights and dignity of the

participants, as well as to promote integrity and responsibility in the production

and dissemination of knowledge.

Respect for people is a principle that recognizes the innate dignity of each

individual and their right to decide about their own life and body. In the context

of scientic research, this translates into the need to obtain informed consent

from all participants before any study is conducted. This implies that researchers

must provide clear and understandable information about the study's objective,

procedures, potential risks and benets, as well as their right to withdraw at any

time without negative repercussions:

- Informed Consent: It is essential that participants understand the nature

of the research and the use that will be made of the data collected. This not

only ensures the protection of individuals, but also promotes transparency

in the investigative process.

- Privacy and Condentiality: Likewise, the privacy of the participants must

be respected, ensuring that their data is handled condentially and that

their identity is not revealed without their explicit consent. The ethical

handling of information is crucial to maintain trust between researchers

and the community.

The principles of benecence and justice are interrelated and essential to

ethical practice in research. Charity refers to the obligation to maximize benets

and minimize risks to participants. This implies a commitment on the part of

researchers to carry out studies that provide social value and that are conducted

responsibly:

- Risk-Benet Assessment: Before starting a study, it is critical to conduct a

thorough assessment of the potential risks and benets. Researchers

should ensure that the intended benets justify any potential harm or

discomfort that participants may experience.

Justice, on the other hand, refers to the equitable distribution of the benets

and burdens of research. It is imperative that all groups, especially those who

have historically been underrepresented or marginalized, have the opportunity

to participate in and benet from research. This means that:

- Equal Access: Participant selections must be conducted in a fair and

equitable manner, avoiding any form of discrimination or exploitation.

Scientic integrity is another fundamental pillar in ethical research. This

principle refers to honesty in the collection, analysis, and presentation of data.

Researchers have a responsibility to report their ndings accurately and

transparently, avoiding any form of misrepresentation:

- Transparency in Publication: Publishing results, even those that are

negative or do not meet initial expectations, is crucial for the advancement

of scientic knowledge. This helps to avoid duplication of eort and to

build a more robust body of knowledge.

- Prevention of Plagiarism and Fraud: Maintaining high standards of

integrity also involves being respectful of the work of others, properly

citing sources and avoiding plagiarism. The scientic community is

valued for its ability to collaborate and develop knowledge within a

framework of respect and ethics.

Respect for people, benecence and justice, and scientic integrity are

essential for the development of ethical and reliable research, which not only

benets the scientic community, but, more importantly, respects and protects

the individuals who participate in it.

3.4 Context and denition of interactive dialogic learning

Throughout history, dialogue has been a central tool in the transmission

of knowledge and in the construction of mutual understanding. The idea of

dialogic learning is nourished by philosophical and psychological theories, such

as those proposed by thinkers such as Freire and Bakhtin, who highlighted the

importance of social interaction in the learning process. Interactive dialogic

learning is based on the premise that knowledge is not something that is

passively transferred from an educator to a learner, but is co-constructed through

the exchange of ideas, perspectives, and experiences (Zhukova et al., 2022).

This approach focuses on creating spaces in which all participants can

express their thoughts, question concepts, and build new understandings in an

environment of respect and collaboration. Through dialogue, students not only

acquire information, but also develop critical skills such as argumentation, active

listening, and empathy.

In addition, interactive dialogic learning manifests itself in a variety of

ways, from small group discussions to open forums in which everyone has a

voice. In this sense, the use of digital technologies has also facilitated the

expansion of these practices, allowing students from dierent contexts and

cultures to interact and learn from each other through online platforms.

Education faces constant challenges in the twenty-rst century, from the need

to prepare students for an ever-changing world of work to the demand for skills

that allow them to adapt and collaborate in a diverse environment. In this

framework, interactive dialogic learning is presented as an eective response to

these demands.

- Promotion of active participation: Unlike traditional teaching models,

where the teacher is the only protagonist, dialogic learning invites

students to be actively involved in their learning process. This gives them

greater autonomy and allows them to assume a leading role, which results

in greater motivation and commitment to learning.

- Development of key competencies: Communication skills, teamwork

and critical thinking are vital for personal and professional development.

Interactive dialogic learning contributes to the formation of these

competencies by promoting an environment where the expression of ideas

and the joint construction of knowledge are valued.

- Inclusion and diversity: In an increasingly multicultural world, it is

crucial that educational practices are inclusive and respect diversity.

Dialogue allows dierent voices and perspectives to be heard, which not

only enriches the learning process, but also fosters respect and tolerance

among students.

- Preparation for democratic life: Education should not only focus on the

acquisition of knowledge, but also on the formation of responsible and

commied citizens. Interactive dialogic learning promotes skills that are

fundamental for active participation in a democratic society, such as the

ability to debate, argue, and reach consensus.

- Adaptation to new technologies: The incorporation of technological tools

in interactive dialogic learning allows expanding the possibilities of

communication and collaboration. Digital platforms oer spaces for the

exchange of ideas and collaborative work, which can further enrich

educational experiences.

Commonly, interactive dialogic learning is presented as an educational

approach that not only responds to the needs of the current context, but also

oers the foundations for a more meaningful, inclusive and transformative

education. Through dialogue and interaction, the aim is to generate learning that

transcends the mere memorization of content, becoming an enriching experience

for all participants.

3.4.1 Fundamentals of Interactive Dialogic Learning

Interactive dialogic learning is a pedagogical approach that relies on

interaction and dialogue among participants as fundamental methods for

acquiring knowledge. To deeply understand this approach, it is essential to

explore the theories of learning that underpin it, the basic principles that govern

it, and how they compare to other learning methods (Nouri, 2014).

Interactive dialogic learning is based on various theories of learning that

highlight the importance of social interaction in the educational process. Some of

the most relevant theories include:

• Constructivism: Proposed by theorists such as Jean Piaget and Lev

Vygotsky, constructivism suggests that individuals construct their own

knowledge through experiences and interaction with others. Vygotsky, in

particular, emphasizes the role of language and culture, stating that

learning occurs in a social context and that interaction with others is key

to developing new skills and concepts.

• Socio-cultural theory: This theory, also formulated by Vygotsky, focuses

on how the social and cultural environment inuences learning.

Interaction with peers and educators in a cordial and collaborative

environment enriches learning, promoting a joint construction of

knowledge.

• Theory of multiple intelligences: Howard Gardner proposed that there

are dierent types of intelligence, implying that students can learn in a

variety of ways. Interactive dialogic learning allows all learning styles to

be reected in an environment where dierent voices and perspectives are

valued.

These theories illuminate the importance of dialogue and collaboration,

highlighting that learning is not an individual process, but a phenomenon that is

enriched by sharing ideas, questions, and solutions. There are several principles

that serve as the foundation of interactive dialogic learning, which are essential

for its correct implementation in the classroom:

• Active participation: Learners are active participants in their learning

process. They are encouraged to express their ideas and opinions, which

encourages greater emotional and intellectual involvement in the content.

• Joint construction of knowledge: Learning is considered a collective

process where all participants contribute, generating new understandings

and meanings through the exchange of ideas.

• Diversity of perspectives: Dialogue allows for the inclusion of multiple

points of view, which enriches learning. This principle is fundamental for

the development of critical thinking, since students must listen, reect and

evaluate dierent arguments.

• Constructive feedback: Interaction involves giving and receiving

constructive criticism. This helps students improve their understanding

and skills, promoting an environment where error is not feared, but

valued as part of the learning process.

• Contextualization of knowledge: This principle focuses on connecting

educational content with the reality and experiences of students. Not only

does this facilitate a beer understanding of the content, but it also

motivates students by seeing the relevance of what they are learning.

3.4.2 Comparison with other learning methods

When comparing interactive dialogic learning with other pedagogical

methods, signicant dierences can be observed:

• Traditional teaching methods: In these methods, the teacher is the central

gure and knowledge is transferred in a unidirectional way, from the

educator to the students. Unlike dialogic learning, student participation is

limited and they are expected to hear and memorize information. This can

lead to supercial learning, in which students are passive and unengaged.

• Collaborative learning: Emphasizes interaction and teamwork, often

focusing on specic tasks and small groups. In contrast, interactive

dialogic learning integrates greater openness to dierent topics and a

space for each voice to be heard in the learning process, creating a more

inclusive environment.

• Project-based learning: This approach promotes research and problem-

solving through team projects. While it may seem similar, interactive

dialogic learning focuses more on the process of ongoing dialogue, while

project-based learning can border dialogue by focusing more on the end

results.

The fundamentals of interactive dialogic learning are rooted in theories that

highlight the importance of social interaction, various principles that encourage

active learning, and a comparison that shows its advantages over traditional

methods and other pedagogical approaches (Chi, 2009). Its implementation not

only transforms the classroom into an enriching learning space, but also prepares

students for a world that values communication and collaboration.

3.4.3 Benets of Interactive Dialogic Learning and Development of

Communication Skills

Interactive dialogic learning has become one of the most valued

methodologies within contemporary educational environments, not only for its

focus on content, but also for the multiple benets it oers to students. Through

dialogue and interaction, students have the opportunity to express themselves,

listen to their peers, and reect on dierent points of view. This dialogic

environment promotes the practice of verbal and non-verbal communication,

facilitating the development of a series of essential competencies, such as:

• Speaking: Students learn to articulate their ideas clearly, using

appropriate vocabulary and building strong arguments to defend their

position.

• Active Listening: The methodology encourages aention and respect for

the opinions of others, reinforcing the ability to actively listen and ask

constructive questions.

• Conict Management: Through dialogue, students learn to handle

disagreements and conicts constructively, developing negotiation and

mediation skills.

By strengthening these communication skills, students are prepared not only

for academics, but also for their personal and professional lives, where eective

communication is critical.

3.4.4 Promotion of critical thinking

Interactive dialogic learning not only focuses on the transmission of

knowledge, but also stimulates critical thinking among students. Dialogue

becomes a tool to question, reect and deepen the contents. Some of the ways in

which this methodology encourages critical thinking include:

• Questioning: Students learn to ask critical questions and question the

information they receive, promoting analysis and the search for evidence.

• Diverse Perspectives: By interacting with dierent points of view,

students develop the ability to analyze and consider multiple perspectives

before reaching a conclusion, enriching their thinking.

• Reection: Debate and discussion help students reect on their own

beliefs and assumptions, allowing them to develop more autonomous and

grounded thinking.

In this sense, interactive dialogic learning not only contributes to a deeper

understanding of the contents, but also prepares students to address complex

problems in their daily lives, building a critical sense that is essential in today's

society. Another benet of interactive dialogic learning is its ability to promote

the inclusion and active participation of all students. In the traditional education

system, some students may feel more isolated or less likely to participate.

However, the dialogic approach seeks to create a safe and welcoming space

where every voice is heard.

• Inclusion of All Students: By structuring activities that encourage

dialogue, you ensure that students of dierent skill levels, personalities,

and cultural backgrounds can contribute. This is especially important in

diverse environments, where it is essential to reconnect and value the

unique experiences of each student.

• Active Participation: Constant interaction allows all students to have the

opportunity to actively participate in their learning. This approach not

only improves their engagement with content, but also increases self-

esteem and condence in their abilities.

• Collaborative Work: Working in small groups or pairs promotes

collaboration among students, creating a sense of community and

belonging within the classroom.

Fostering inclusion and active participation ensures that every student can

benet from the learning process, making education a more equitable and

enriching experience for all. It facilitates the development of communication and

critical skills, but also creates an inclusive environment that encourages all

students to actively participate, these aspects constitute a solid foundation for a

more comprehensive and eective education in the current context (Molina et al.,

2021).

Chapter IV

Ethics in scientic research

Scientic research is an essential pillar of the advancement of human

knowledge, but its practice must be guided by sound ethical principles. These

principles not only protect research participants, but also ensure that the results

are valid and reliable. The principle of autonomy focuses on respect for the

individual decisions of the participants. Each person has the right to make

decisions about their own life and body, which implies that they must be fully

informed about the research in which they are participating. Informed consent is

the concrete manifestation of this principle.

The voluntary nature of participation, emphasizing that participants can

withdraw at any time without penalty. Obtaining informed consent is not just a

formality; It is an ongoing process that must be re-evaluated and maintained

throughout the research. Researchers should be aentive to changes in the

situation of the participants and ensure that they continue to give their consent

freely and voluntarily.

It is especially critical in research involving vulnerable populations, such

as children, the elderly, or those with cognitive disabilities. In these cases,

researchers should work together with guardians or legal representatives,

ensuring that the rights and wishes of the participants are respected (Gordon,

2020). The principles of benecence and nonmalecence are intrinsically related,

charity implies the obligation to maximize prots and minimize harm in

research. Researchers should design their studies in such a way that the risks are

justied by the potential benets that will accrue from them.

On the other hand, the principle of non-malecence refers to the obligation

not to cause intentional harm. This principle underlines the duty of researchers

to avoid any act that may be harmful to participants. To ensure compliance with

these principles, it is essential that researchers conduct risk assessments before

starting their studies. The principle of justice refers to equity in the distribution

of the benets and burdens of research. Researchers must ensure that there are

no specic groups that benet disproportionately or are subjected to unfair or

exploitative practices. This implies that:

- The populations selected to participate in the research must be

representative of the general population to which the results will be

applied.

- The benets of research should be available to all participants and not just

those from privileged classes.

- It must be ensured that the vulnerabilities of certain groups are not

exploited but are protected.

Equality is also manifested in access to research. All individuals should have

the opportunity to participate in studies that could improve their health and well-

being, without discrimination on the basis of race, gender, religion, or

socioeconomic status (Togioka et al., 2024). Respect for these ethical principles is

not only a legal obligation, but a moral responsibility that researchers assume

when they dedicate themselves to the pursuit of knowledge. By adhering to these

principles, fairer and more responsible research practices are promoted, which

contribute to the progress of science and the improvement of the quality of life in

society. Scientic research, especially in the elds of biomedicine and social

sciences, is governed by a series of ethical rules and regulations that seek to

protect the dignity, rights, and well-being of participants.

Among the most recognized are the Declaration of Helsinki and the Standards

of Good Clinical Practice (GBPC). These regulations establish frameworks for the

ethical conduct of research and ensure that they are carried out with the utmost

respect for the participants.

4.1 Declaration of Helsinki

The Declaration of Helsinki, adopted by the World Medical Association

(WMA) in 1964, is considered a fundamental pillar in the ethics of medical

research. This standard has been revised on multiple occasions, progressively

incorporating aspects that respond to contemporary ethical challenges (Carlson

et al., 2004). Its main objective is to provide ethical principles that guide research

on human beings and safeguard the rights of participants.

Some of the most salient principles of the Declaration of Helsinki are:

- Respect for persons: The Declaration underscores the importance of

informed consent. Every research participant must provide their consent

freely, informed, and voluntarily, which implies that they must fully

understand the risks, benets, and purpose of the study.

- Benecence: Researchers should ensure that the benets of the research

outweigh the potential risks to the participants. This involves a careful

assessment of the consequences of research for both individuals and

society.

- Justice: The equitable distribution of the benets and burden of research

is another of the fundamental premises. No particular group should be

favored or disadvantaged in the selection of participants.

- Transparency: Researchers are required to report any conicts of interest

and their funding, which improves condence in the research process and

the results obtained.

Over the years, the Declaration of Helsinki has been a model to follow for

many legislations and guidelines in the eld of medical research around the

world. Its impact extends to multiple research modalities and its application is

reected in the development of local regulations in dierent countries.

The Good Clinical Practice Standards (GBPC), established by the

International Conference on Harmonization of Technical Requirements for the

Registration of Medicinal Products for Human Use (ICH), are guidelines that

ensure quality and ethics in the conduct of clinical trials (Vijayananthan &

Nawawi, 2008). These standards not only seek to protect the rights of

participants, but also to guarantee the integrity of data and the production of

reliable results:

- Study Design: Clinical trials are required to be designed so that research

objectives and hypotheses are clear and risks to participants are

minimized.

- Supervision and monitoring: It is critical that research is overseen by an

independent ethics commiee and that adequate monitoring is

implemented during all phases of the study.

- Documentation and data archiving: The GBPCs mention the importance

of maintaining detailed and accurate records of all phases of the clinical

trial, allowing for audit and peer review that ensures transparency of

results.

- Informed consent: As in the Declaration of Helsinki, informed consent is

an essential requirement. Each participant should be properly informed

and understand the scope of the study, as well as their rights.

The implementation of the Standards of Good Clinical Practice has

promoted greater standardization of criteria in research at an international level,

fostering condence in the results obtained and ethics in the treatment of

participants. These standards are crucial, especially in the context of the

globalization of research, where clinical trials often involve multiple authorities.

In general, both the Declaration of Helsinki and the Standards of Good

Clinical Practice are fundamental for ethical regulation in contemporary scientic

research. These frameworks not only ensure respect for the rights and dignity of

participants, but also promote the integrity and quality of the data obtained,

which is crucial for the advancement of science and medicine.

4.2 Ethical challenges in specic areas of research

Scientic research is constantly evolving and, with it, ethical aspects arise

that require special aention, especially in sensitive areas such as biomedical

research, social sciences and genetic manipulation and biotechnology. Each of

these areas presents its own ethical dilemmas, which must be carefully

considered to ensure that research not only produces knowledge, but also

respects the rights and dignity of human beings and the environment. Biomedical

research is fundamental to the advancement of medicine and public health

(Ayanoğlu et al., 2020).

Now, researchers need to make sure that participants fully understand the

potential risks and benets of the studies they are about to get involved in. This

is especially crucial in clinical trials where new drugs or treatments can be tested.

It is essential to ensure that research does not exploit vulnerable groups, i.e. in

the case of clinical trials in developing countries where regulations may be lax.

Here the issue of equity arises, as it is critical that the benets of research are

distributed fairly and that disadvantaged populations are not disproportionately

represented in studies that may not adequately benet those same groups.

In this context, ethics also come into play when considering data privacy.

The collection of medical data is crucial to the advancement of research, but

rigorous measures are often required to protect the condentiality of patient

data. Researchers need to be sensitive to how their studies can inuence the

communities and groups they are researching.

Informed consent is also crucial in this area, but it is even more complex,

as it is often dealing with populations that may not have the same capacity for

understanding, such as minors or people with intellectual disabilities. This raises

the question of how to approach the ethics of consent and when consent from a

legal guardian is required. The ethics of social science research is also questioned

by the potential for bias. Researchers need to be alert to their own perceptions

and beliefs, and how these may inuence research. Work in this eld can often

involve observing groups in sensitive situations, raising concerns about

exploitation and misuse of information.

Ethics and deontology in biomedical research, social sciences, and genetic

manipulation are complex and multifaceted. To address these conicts, it is

essential that the scientic community maintains an open dialogue on ethics and

that regulatory frameworks are established to guide research towards

responsible and respectful practices. Only in this way can we advance knowledge

without undermining human dignity and social well-being.

Ethics in scientic research has been the subject of scrutiny throughout

history. As science advances, concerns arise about how research is conducted and

respect for the subjects involved. This section will focus on some of the most

notorious historical scandals involving ethical violations and the lessons learned

from recent cases.

4.3 The future of ethics in scientic research

These innovations, while promising, require a constant re-evaluation of

ethical principles to ensure that scientic progress does not lead to harmful

consequences for society or the environment. Technological innovations, such as

gene editing, biotechnology, and advances in areas such as nanotechnology, have

transformed the way scientic studies are conducted. Some of the main ethical

dilemmas are:

- Genetic manipulation: The possibility of altering the DNA of organisms,

including humans, has opened a wide debate about the limits of what is

ethically acceptable. Through genetic modication, proles of equity,

identity, and unintended consequences on future generations are

examined.

- Privacy and data: With the increasing use of technologies and applications

that collect personal data, there are concerns about privacy protection and

the possibility of this data being used in an abusive manner. Scientic

research must ensure that the use of personal data is consensual and that

the rights of individuals are protected.

- Sustainability and the environment: Technological innovation also

presents trade-os with respect to the impact on the environment. While

new technologies can oer solutions to global problems such as climate

change, they can also lead to unsustainable practices that harm the delicate

balance of ecosystems.

To address these emerging ethical scenarios, exible regulatory

frameworks are needed that adapt quickly to ever-changing circumstances.

Research ethics should be reinforced with continuous training for researchers

and policy makers, ensuring that ethical dimensions are always considered in

decision-making.

One of the most revolutionary elds today is articial intelligence (AI). As

AI becomes increasingly integrated into scientic research, there is also a need to

establish clear ethical principles. Some of the ethical dilemmas related to AI

include:

- Transparency and applicability: As AI models become more complex and

less understandable, the question arises of how decisions are made and

what biases may be implicit in these processes. It is crucial that research

uses models that are transparent and provide understandable

explanations for their results.

- Accountability: Running AI-assisted research can lead to ambiguity in

accountability, especially in cases of errors or biases in the results.

Determining who is responsible – the technology developer, the

researcher, or the institution – is critical to ensuring accountability.

- Impact on employment: Automating tasks through AI can aect jobs in

science and other sectors. Research ethics must be extended to consider

how these technologies might aect access to job opportunities, equity,

and

Policies should include diverse perspectives, including those from historically

marginalized groups, to ensure a comprehensive approach. In conclusion, the

future of ethics in scientic research will depend on our ability to adapt to rapid

technological changes and address the ethical faces that arise from them (Roche

et al., 2023). Critical reection on these issues will not only foster responsible

advancement in the eld of science but will also help build a more ethical and

just society in the twenty-rst century.

Fundamental ethical principles, such as autonomy, benecence,

nonmalecence, and justice, become indispensable guides to ensure that science

not only seeks knowledge for knowledge's sake, but does so responsibly and with

respect for the subjects involved and society in general. Reection on ethics in

research not only addresses existing regulations, but also considers the broader

implications of scientic advances on human life and the environment.

However, ethical research is essential to maintain public trust in science; An

ethical scandal can have lasting repercussions, ranging from the loss of credibility

of the scientic community to widespread skepticism towards scientic

advances. In the present, we are faced with new and complex ethical questions

that require introspection and meticulous evaluation. Genetic manipulation,

articial intelligence and research in sensitive areas such as mental health or data

privacy are just a few examples that raise deep ethical questions. It is essential

that researchers stop to reect on the impact of their work on people's lives and

society. The key lies in a preventive approach that seeks to anticipate and mitigate

damage before it materializes.

Also, the perspective of multidisciplinary and collaborative research can

enrich the dialogue on these ethical issues. Scientists, philosophers, psychologists

and representatives of civil society have much to contribute to the ethical

discussion. To ensure that research ethics is not merely regulatory compliance,

but is deeply integrated into all phases of research, the following

recommendations are suggested:

- Continuing education: Researchers should participate in ethics training

programs, which include not only existing regulations, but also case

studies and discussions on contemporary ethical trade-os.

- Promoting ethical culture: Institutions should foster an environment that

values and promotes ethics, where researchers feel free to raise ethical

concerns without fear of repercussions.

- Active and diverse ethics commiees: Ethics commiees should be

multidisciplinary and have members who can bring dierent

perspectives. This will allow for a more complete evaluation of research

protocols, considering both scientic and human aspects.

- Involve the community: The participation of interest groups and the

community in research is essential. Not only does it improve the quality

of studies, but it also helps to legitimise the results and increase public

trust in science.

- Adaptive ethical assessments: Given the speed with which science is

advancing, assessment frameworks must be established that are exible

enough to adapt to new technologies and research methods.

- Transparency: Transparency in research processes, the publication of

methodologies and results, as well as the disclosure of conicts of interest,

are essential to maintain society's trust in science.

Ethics in scientic research requires constant and deliberate aention. It is a

shared commitment between researchers, regulators and society to ensure that

the advancement of knowledge is carried out in a responsible and fair manner.

Only through this joint eort will it be possible to build a scientic future that is

in harmony with the human and ethical values that govern our society.

Today, technology has advanced at an unprecedented speed. This dizzying

growth has transformed the way we live, work and relate to each other.

Information and communication technologies (ICTs) are one of the clearest

examples of how research in science and technology aects people's daily lives.

Digital platforms, social media and collaborative tools allow immediate access to

a vast ocean of information, which has redened the way we learn and

communicate.

Research in cybersecurity and digital ethics has become especially relevant,

given the need to protect users' privacy and personal information. Likewise,

articial intelligence and automation are revolutionizing various sectors, from

manufacturing to health services, but they also bring with them challenges such

as labor obsolescence and misinformation (Al Kuwaiti et al., 2023).

4.4 Research as a driver of development

Research in science and technology not only drives the advancement of

knowledge but is also an engine of economic development. Countries that invest

signicantly in research and development (R+D) tend to experience higher

growth and competitiveness. According to UNESCO reports, those that allocate

a high proportion of their GDP to this area nd beer opportunities for

innovation, which in turn generate new companies, jobs and a more robust

economy.

However, the gap in investment in R+D between developed and

developing countries is notable. It is crucial that governments and institutions

prioritize research as a development strategy. Encouraging science and

technology education from childhood, promoting collaboration between

universities and companies, and establishing policies that support research are

necessary steps to level the playing eld.

Research in science and technology is an open door to a future full of

possibilities. With the commitment of scientists, researchers, eective policies,

and a well-informed citizenry, we will be able not only to face today's challenges,

but also to build a more sustainable and equitable world. Continuous exploration

and insatiable curiosity are the engines that drive progress, making investment

in science and technology an inescapable priority for the societies of the future.

Thanks to these eorts, signicant advances have been generated that have

transformed our lives, promoting not only economic growth, but also the

improvement in people's quality of life. This section will focus on recent

technological developments and their impact on society.

In the post-Covid-19 era, research has allowed for rapid evolution in

various technological areas. Some of the most notable developments include:

- Biotechnology: Biotechnology, which combines biology and technology,

has had a notable impact on medicine and agriculture. Innovations such

as CRISPR gene editing have opened new frontiers in the treatment of

diseases and in the creation of more resistant crops. These technologies

make it possible to address complex problems such as hunger and

hereditary diseases.

- Renewable energy: Research into renewable energy has contributed to the

creation of more ecient and accessible technologies. Solar and wind

energy have advanced to the point of being competitive with fossil fuels

in terms of cost and eciency. This not only helps combat climate change,

but also promotes energy independence for countries.

- Information and communication technologies (ICT): With the rise of the

internet and digitalization, ICTs have changed the way we communicate,

work, and learn. Research into the 5G network and the next generation of

the Internet has enabled faster and more reliable connectivity, favoring the

development of new applications and services.

These advances are not only the result of creativity and innovation, but also

of collaboration between scientists, engineers, and companies. Investment in

research and development (R+D) is therefore essential to maintain and promote

these innovations. The impact of these technological advances on society is

profound and multifaceted. Among the main aected areas, the following stand

out:

- Health: Advances in biotechnology and medicine have allowed the

development of personalized treatments, improved the eectiveness of

treatments and reduced side eects. In addition, vaccine research has been

crucial in facing pandemics, as evidenced by the rapid creation of COVID-

19 vaccines, which have saved millions of lives.

- Education: The digitalization of education has democratized access to

knowledge. Online learning platforms, supported by research in

pedagogy and technology, have made it easier for students from dierent

parts of the world to access educational quality that was previously

unthinkable. This educational transformation must continue to evolve to

adapt to the needs of the 21st century.

- Economy: Technology has enabled the creation of new industries and jobs,

stimulating economic growth. Technology-based startups have

proliferated, from delivery apps to remote work platforms, contributing

to the diversication of labor markets.

- Daily life: The incorporation of technology into daily life has improved the

quality of life. From smart appliances to mobile apps that make it easier to

manage personal time and resources, technological advancements are

enabling people to live more comfortable and ecient lives.

For this reason, it is important to mention that the impact of research in

science and technology also entails new teaching-learning schemes. Ethical

questions, particularly in areas such as AI and biotechnology, require careful

aention to ensure that innovations are used responsibly and equitably. Research

in science and technology is not only vital for technical advances, but also plays

a critical role in building a more sustainable and equitable future. Collaboration

across sectors, as well as continued support for research, will be essential to

further harness the transformative potential of these disciplines.

4.5 Scientic Research Methodologies

Scientic research is a systematic and methodical process that aims to

generate knowledge through observation, experimentation, and analysis.

Scientic research methodologies are critical, as they determine how a hypothesis

is posed, data is collected, and the results are analyzed (Barroga & Matanguihan,

2022). There are two methodological approaches: quantitative and qualitative

methods, each with distinctive characteristics and applications in dierent elds.

In addition, interdisciplinarity has become a crucial element in modern research,

allowing diverse perspectives to be integrated in the search for innovative

solutions.

Quantitative methods focus on the collection and analysis of numerical

data to understand phenomena and establish relationships between variables.

These methods are particularly useful when seeking to generalize results from

representative samples and require precision in estimates. Some characteristics

and processes associated with quantitative methods include:

- Experimental Design: In this approach, independent variables are

manipulated to observe their eects on dependent variables. This allows

us to establish cause-and-eect relationships.

- Surveys and Questionnaires: Surveys are a common tool in quantitative

research, as they allow data to be collected from a large number of subjects

in a structured way. The questions can be multiple-choice, Likert scales,

among others, facilitating statistical analysis.

- Statistical Analysis: Once the data is collected, statistical techniques are

used to interpret the results. This includes measures of central tendency,

variability, regressions, and analysis of variance (ANOVA), among others.

- Validity and Reliability: Quantitative methods strive to be valid and

reliable. Validity refers to the ability of the study to measure what it

purports to measure, while reliability refers to the consistency of results

over time.

Quantitative methods are widely used in disciplines such as psychology,

sociology, education, and health, providing robust empirical evidence that can

inuence policies and practices.

Unlike quantitative methods, qualitative methods seek to understand

phenomena in depth through the collection of non-numerical data. It is ideal for

exploring experiences, perceptions, and meanings that people aribute to certain

events or situations. Some key aspects of qualitative methods are:

- In-Depth Interviews: This technique allows researchers to gain a detailed

understanding of the participant's perspective. The interviews are exible

and can be adapted to the interviewee's answers, allowing clarications

and deepening of the topics of interest.

- Focus Groups: A group of people meets to discuss a specic topic guided

by a moderator. This encourages interaction and allows a variety of

opinions and experiences to be explored.

- Content Analysis: It focuses on identifying paerns and themes in texts,

speeches, or any type of content. This technique is vital for deciphering

underlying meanings and tendencies in communication.

- Participant Observation: In this technique, the researcher engages in the

study environment, observing as he or she participates. This provides a

richer and more contextualized vision of social reality.

Qualitative methods are essential in elds such as anthropology, sociology,

education, and health, where understanding the human experience is critical.

4.5.1 Interdisciplinarity in Research

Interdisciplinarity is an approach that combines knowledge and methods

from dierent disciplines to address complex problems that cannot be solved by

a single area of study. This multidimensional approach has gained great

relevance in current scientic research for several reasons:

- Comprehensive approach: It allows a more complete understanding of the

phenomena, integrating various perspectives and methodologies.

- Innovation: The combination of dierent disciplines can produce new

ideas and innovative solutions, which is essential in an ever-changing

world.

- Collaboration: It encourages collaborative work between researchers from

dierent areas, creating a dialogue that enriches knowledge and

methodological approaches.

In this line, scientic research methodologies, whether quantitative or

qualitative, and the interdisciplinary approach are key tools in the search for

knowledge and solutions to the problems facing society. The integration of these

methodologies allows for a richer and more diverse exploration of the world

around us and contributes signicantly to the advancement of science and

technology. Science and technology have become fundamental pillars of human

development, oering solutions to complex problems and improving people's

quality of life (Bryda & Costa, 2023).

Sustainable innovation refers to the development of technologies and

practices that meet present needs without compromising the ability of future

generations to meet their own needs. This concept has become increasingly

relevant in a world facing problems such as climate change, resource scarcity,

and environmental pollution. The search for sustainable solutions implies a

change in thinking in the way technologies are conceived and developed. Some

of the key strategies in this area are:

- Renewable energy: The transition to clean energy sources, such as solar

and wind, is crucial to reducing dependence on fossil fuels. Innovations in

energy storage and energy eciency also play an important role in this

transition.

- Circular economy: This approach promotes the reduction, reuse and

recycling of materials, thus minimizing waste and the exploitation of

natural resources. Companies are beginning to adopt business models that

integrate circular economy principles into their production and

consumption.

- Sustainable agriculture: With the increase in the world's population, it is

critical to develop agricultural practices that maximize food production

without causing signicant damage to the environment. Biotechnology

and agroecology are being used to make agriculture more ecient and

sustainable.

Resistance to change, lack of funding, and lack of adequate government

policies are just some of the situations that need to be addressed. It is essential

that governments, businesses and civil society work together to foster an

environment that is conducive to sustainable innovation (Fallah et al., 2022).

Research ethics is a critical component that is often overlooked in scientic and

technological development. As science advances, new questions and concerns

arise about the responsible use of technology and the impact of scientic studies

on society. Some of the most important ethical aspects to consider include:

- Informed consent: In the eld of biomedical research, it is crucial to ensure

that study participants are fully informed about the risks and benets

before giving consent. A lack of transparency can lead to the exploitation

of vulnerable individuals and communities.

- Privacy and personal data: With the rise of digital technologies and the

collection of large volumes of data, privacy protection has become a

central concern. Research must ensure that individual rights are respected,

and that data is used ethically and securely.

- Inclusive approaches: It is essential that research considers diversity and

seeks to include dierent cultural and social perspectives. The exclusion

of certain groups can lead to biases in outcomes and the perpetuation of

inequalities.

In addition, research ethics is not limited to the aforementioned aspects. It

also covers issues related to the social responsibility of scientists and technology

companies, as well as how innovations aect ecosystems and communities.

Research ethics requires proper regulation, as well as active engagement by

researchers and technology developers to ensure that their work contributes

positively to society. Education in research ethics is vital to preparing the next

generation of scientists and technologists to navigate these complex dilemmas.

Therefore, sustainable innovation and ethics in research are two of the most

important parameters facing science and technology today. Collaboration across

dierent sectors and disciplines, coupled with a strong commitment to

sustainability and ethics, is essential to address these challenges eectively and

build a brighter future for all.

4.5.2 Social responsibility in research

Scientic research is a fundamental pillar of modernity, and its social

impact is undeniable. Despite the increase in social responsibility in research, it

is an urgent need in an increasingly interconnected world. This approach not

only promotes the well-being of society, but also contributes to environmental

sustainability and equitable social development. In this context, it is necessary to

explore how research can be oriented towards the common good and how

sustainability and responsibility are key elements in this process (Haferkamp &

Smelser, 1992).

Research for the common good refers to those initiatives that seek to

generate knowledge and solutions to problems that aect society as a whole. This

type of research should not be seen only as an instrument for generating

economic gains, but as a tool at the service of humanity. Therefore, the social

impact of research must be measured not only in terms of its contribution to

scientic progress, but also in terms of how it benets diverse communities.

Sustainability has become an unavoidable concept in contemporary

research. This translates into the need to conduct research that is not only

eective in its immediate objectives, but also respectful of the environment and

natural resources:

- Sustainable Research: Sustainable research involves using methods that

minimize environmental impact and promote resource conservation. For

example, the use of renewable energy in research projects can signicantly

reduce the carbon footprint.

- Intergenerational Responsibility: In addition, social responsibility in

research includes a long-term approach. The decisions made must

consider their consequences in the short, medium and long term. This is

reected in concepts such as intergenerational responsibility, where

researchers must act in such a way that they do not compromise the ability

of future generations to meet their needs.

- Global collaboration: Sustainability also requires collaboration across

borders. Research that addresses global problems such as climate change

or social inequality requires the cooperation of various actors, including

governments, NGOs, the private sector and local communities. This

collaborative approach allows for the exchange of knowledge and

resources and enriches the research process.

- Research Ethics: Ethics is a fundamental component of responsibility in

research. This implies that researchers must be transparent in their

methods and results, as well as consider the social impact of their work.

Establishing a culture of integrity and accountability not only benets

research, but also builds trust in the community.

Social impact and sustainability are two sides of the same coin in the eld of

research. The search for the common good and the satisfaction of social needs

must be the pillars on which research initiatives are built. The trend towards

research aimed at solving social problems, together with a focus on sustainability

and ethics, can signicantly transform the way research is conducted (Fallah et

al., 2022).

Thus, researchers have a responsibility to contribute to the well-being of

society, ensuring that their actions do not harm the environment or compromise

the opportunities of future generations. This approach will not only foster a more

just and equitable world but will also ensure that science and research continue

to be engines of progress for all humanity.

Collaboration and transparency are two fundamental pillars of social

responsibility in research. Both practices not only guarantee scientic integrity,

but also foster an environment of trust between researchers, institutions and

society in general. This commitment to collaboration and transparency helps

make research more accessible, understandable and benecial to all.

This multidisciplinary collaboration refers to the cooperation between

dierent disciplines and areas of knowledge in the development of research

projects. Some of the benets of this practice are:

- Broader approaches: Combining diverse perspectives allows a problem to

be approached from multiple angles, which can result in more eective

and sustainable solutions.

- Innovation: The interaction between dierent disciplines often leads to

innovation, by allowing ideas to intersect and transform into new theories

and practical applications.

- Resource optimization: By joining forces, institutions and researchers can

maximize the use of resources, avoiding duplication of eorts and

boosting research results.

- Leveraging talent: The ability to work with experts from dierent areas

increases a team's ability to conduct high-quality research.

To carry out a successful multidisciplinary collaboration, it is essential to

establish eective and open communication between all participants. This

involves not only exchanging information, but also understanding and

respecting the dierent methodologies and approaches that each discipline

brings. In addition, it is crucial that there is clear leadership that keeps the focus

on common goals and facilitates decision-making.

Transparency in research processes is a fundamental principle that ensures

that the methods, data and results of a research are accessible and

understandable to the public. A lack of transparency can lead to

misunderstandings, mistrust, and replication of errors in future studies. Some of

the main elements that characterize transparency in research are:

- Data access: Publishing and making accessible the data collected during

the research process, allowing other researchers to review, analyze and

use them for their own projects.

- Open review: Encourage a peer review process that is open and

transparent, meaning that both reviewers and authors must be

identiable, thus promoting constructive dialogue.

- Clear methodology: It is essential that the methodology used in the

research is described in a clear and detailed manner, so that other

researchers can replicate the study if they wish.

- Publication of negative results: Publishing all results, even those that do

not support the original hypotheses, is crucial to avoid publication bias

and provide a complete picture of the research area.

Transparency not only benets the scientic community, but also builds

society's trust in research. When people see that researchers are open about their

methods and results, they are more likely to trust the ndings and those applied

in policies or practices. This is especially important in areas of great social

relevance, such as health, education and the environment.

In addition, transparency in research also includes ethical aspects, such as the

declaration of conicts of interest and the nancing of projects. Many times,

research results can be inuenced by the interests of funders. Therefore, it is

crucial that researchers declare any relationships that may inuence their work,

thus ensuring that the results are perceived as more valid and reliable.

The need for greater transparency in research has led to the creation of

initiatives and platforms that promote these principles. A notable trend in the

eld of research has been the creation of study registers, in which researchers can

record their protocols and methods before beginning research. These records

allow others to review and evaluate the decisions made, thus encouraging a more

rigorous and critical approach to science (Prager et al., 2019).

Promoting collaboration and transparency in research requires not only

changes in the individual practices of researchers, but also a cultural shift in

institutions and in the scientic community at large. Universities, research

centers and funding agencies must create incentives and policies that favor these

practices. This may include incentives for data publication, recognition of

collaborative eorts, and the implementation of transparency principles in the

evaluation of research projects.

Therefore, multidisciplinary collaboration and transparency in processes are

fundamental for social responsibility in research. Encouraging joint work

between dierent disciplines and ensuring the accessibility of methods and

results will not only raise the quality of research, but also promote an

environment of trust between researchers and society. As we face increasingly

complex situations, commitment to these practices will be decisive in ensuring

that research generates a positive and lasting impact on society.

4.5.3 Education and Training in Ethics

Education in ethics is a fundamental pillar in the training of socially

responsible researchers. In a world where science and technology are advancing

rapidly, it becomes imperative that professionals are not only competent in their

areas of study, but also possess a solid understanding of the social, moral, and

ethical implications of their work. Ethics training should be a continuous process,

from basic education to specialization in various disciplines, including social

sciences, biomedicine, engineering, and more. Here we explore the key elements

of ethics education and training in the context of research.

Research ethics refers to the principles and norms that guide the behavior

of researchers. This includes integrity, honesty, fairness, and respect for research

subjects. Violation of these principles can not only aect the outcome of a study

but can also have broader negative consequences on society. Therefore, it is

essential that researchers are educated about ethics from the beginning of their

careers.

Educational institutions have a crucial role to play in teaching ethics to

future researchers. Incorporating ethics courses into academic programs can help

familiarize students with the various ethical issues that can arise in research.

These courses should include not only ethical theory, but also practical case

studies that reect ethical alternatives.

An important way to formalize the commitment to ethics in research is

through the creation and adoption of an institutional code of ethics. This code

must be accessible and understandable, and all members of the institution must

be trained on its content and application. Ethical socialization is an ongoing

process that involves not only formal education, but also interaction with

colleagues and mentors. The culture of an institution can signicantly inuence

the ethical training of researchers. Instances of ethical behavior modeling by

mentors and leaders in the eld are crucial for the ethical socialization of new

researchers (Chetwynd, 2024).

Technological progress poses new ethical challenges in research. With the

management of digital education, issues related to privacy, the use of data and

articial intelligence have arisen. Therefore, ethics education must adapt and

evolve with these new realities.

In general, ethics education and training are essential to develop

responsible researchers commied to social welfare. Academic institutions and

research bodies must work together to infrastructure programs that not only

teach ethical philosophy, but also promote a culture of social responsibility in all

aspects of research. Ethics should not be an add-on, but an integral and

fundamental part of the research process from start to nish.

By fostering an environment where ethics is a priority and where

researchers feel empowered to face ethical dilemmas, we can ensure that research

not only advances science and technology, but also contributes positively to

society at large. Ethics training should be seen as an investment in the future,

which will guarantee research that can be respectful, responsible and that

generates benets for all.

Conclusion

The incorporation of articial intelligence (AI) into educational

management and innovation represents a signicant transformation with

revolutionary potential to change the way we teach and learn. In this analysis,

we saw how articial intelligence not only streamlines administrative processes

and resource management, but also improves the personalization of learning,

adapting content to the needs of each student, creating and delivering virtual

learning activities eectively. The most notable discoveries include:

- Increase eciency: The use of AI-based tools allows educational institutions

to operate more eciently, allowing teachers to focus on the more creative

and interpersonal aspects of teaching.

- Adaptation to individual strategies: AI systems can analyze large amounts of

data to oer unique learning experiences that adapt to each student's

learning pace and style.

- Ethical issues and access: It is important to address ethical issues related to

data privacy and equitable access to these technologies. The digital divide

can perpetuate inequalities that already exist in the education system.

Overall, while AI poses challenges that need to be carefully managed, its

potential to transform education is undeniable and ensures a more inclusive and

adaptable future for all learners. The key will be to nd a balance between

innovation and the ethical values that guide its implementation. The future of

articial intelligence (AI) in education opens up a space full of possibilities and

radical transformations that have the potential to redene the way we teach and

learn.

As technology advances, we are likely to see greater integration of AI into

various aspects of the education system, from management to personalization of

learning. One of the most promising aspects is the personalization of learning.

Thanks to advanced algorithms, the educational platform can adapt to the

individual needs of students, providing resources and activities tailored to their

strengths and weaknesses. This will ensure more eective and motivated

learning, and each student will progress at their own pace.

In addition, the introduction of virtual assistants can change the way teachers

interact with students. Not only does this improve student learning, but it also

gives teachers more time to focus on more strategic and creative activities.

Social responsibility in research refers to the involvement of researchers not

only in the development of knowledge but also in the well-being of society as a

whole. This concept assumes that research results should be used to improve

quality of life, promote equality and promote sustainable development. Research

faculty must realize that their work can have a signicant impact on

communities, ecosystems, and public health. Therefore, the most important

responsibilities of researchers are to ensure that their research is conducted

ethically and that results are reported in a transparent and accessible manner.

This includes:

- Community Engagement: Involve aected communities in the research

process to ensure that their needs and concerns are considered.

- Equity in research: Ensuring that vulnerable populations are not exploited

and that the benets of research are distributed fairly.

- Sustainability: Conduct research that contributes to environmental,

economic, and social sustainability, minimizing harm and maximizing

benets.

In conclusion, social responsibility in research is essential to build a bond of

trust between scientists and society, allowing the knowledge generated to be

used for the common good. However, the future of AI in education also faces

challenges that need to be addressed, such as data protection, student privacy,

and teacher training for new technological realities. It is important to develop

policies and ethical frameworks to ensure the responsible use of AI in schools.

76 
 
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Intelligent Systems, 18(2), 57-61. hps://doi.org/10.14313/jamris/2-2024/13  
   

This edition of " Artificial intelligence in education management: Ethics and

social responsibility" was completed in the city of Colonia del Sacramento

in the Eastern Republic of Uruguay on December 12, 2024