# Normative framework for ethical and trustworthy AI in higher education: state of the art

María Obdulia González Fernández, María Asunción Romero-López, Natalia Fátima Sgreccia, María José Latorre Medina

<figure><img src="image_1.png" /></figure>

## To cite this version:

María Obdulia González Fernández, María Asunción Romero-López, Natalia Fátima Sgreccia, María José Latorre Medina. Normative framework for ethical and trustworthy AI in higher education: state of the art. Revista Iberoamericana de Educación a Distancia, 2025, 28 (2), 10.5944/ried.28.2.43511. hal-05107622

HAL Id: hal-05107622

https://hal.science/hal-05107622v1

Submitted on 11 Jun 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
# Normative framework for ethical and trustworthy AI in higher education: state of the art

## Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión

<figure><img src="image_3.png" /></figure>

María Obdulia González-Fernández - Universidad de Guadalajara, UDG (Mexico)

María Asunción Romero-López - Universidad de Granada, UGR (Spain)

Natalia Fátima Sgreccia - Universidad Nacional de Rosario, UNR (Argentina)

María José Latorre Medina - Universidad de Granada, UGR (Spain)

## ABSTRACT

The use of artificial intelligence (AI) in higher education represents an emerging and continuously evolving field. Current research highlights the critical synergy required between ethics and appropriate use in the academic application of AI. Therefore, a documentary study is presented to investigate the regulatory and ethical frameworks of the use of AI in higher education institutions. A systematic literature review was conducted following the guidelines and procedures of the PRISMA model. The information search was carried out in the databases of Scielo, Scopus, Redalyc, among others, which were subsequently exported to Mendeley and Rayyan platforms for analysis and categorization. In the screening process, 28 articles were selected, classified into four categories: ethical challenges and risks, regulatory frameworks, ethical training and didactical models, utilizing the MAXQDA software. Additionally, a bibliometric analysis (VOSviewer) was performed to identify major research trends in this domain. In general, the results show, on the one hand, the importance of generating specific policies and regulations that allow the appropriate use of AI in university contexts. On the other hand, the need for training of the entire educational community involves incorporating its responsible use. The academic debate on how to balance technological innovation with ethics, inclusion, and sustainability remains an open and pressing challenge.

**Keywords:** artificial intelligence; higher education; ethics; legislations.

## RESUMEN

La investigación sobre el uso de la inteligencia artificial (IA) en instituciones educativas es un área emergente y en evolución. En la actualidad se ve la necesidad de analizar la sinergia entre la ética y el adecuado uso de la IA en el contexto académico. Por ende, se presenta un estudio documental para indagar sobre los marcos normativos y éticos del uso de la IA en las instituciones de educación superior. Se realizó una revisión sistemática de literatura bajo las directrices y procedimientos del modelo PRISMA. La búsqueda de la información se realizó en las bases de datos de Scielo, Scopus, Redalyc, entre otras, que posteriormente fueron exportados a Mendeley y procesados en Rayyan con la intención de analizarlos y categorizarlos. En el proceso de cribación se seleccionaron 28 artículos clasificados en cuatro categorías: desafíos y riesgos éticos, marcos normativos, formación ética y modelos didácticos. Se utilizó el software de MAXQDA para su procesamiento. Además, se realizó un análisis bibliométrico (VOSviewer) que permitió conocer las principales tendencias en torno a la temática. En general, los resultados manifiestan, por un lado, la importancia de generación de políticas y regulaciones específicas que permitan el uso adecuado de la IA en contextos universitarios. Por otro, la necesidad de formación de toda la comunidad educativa involucrada, incorporando su uso responsable. El debate académico sobre cómo afrontar el desafío de equilibrar la innovación tecnológica con la ética, la inclusión y la sostenibilidad continúa abierto.

**Palabras clave:** inteligencia artificial; educación superior; ética; normativa.
# INTRODUCTION

Recent academic literature highlights the key role that technologies play in the field of higher education (García-Fuentes et al., 2024; Villegas & Delgado, 2024), as well as their use by both educators (Sánchez, 2023) and university students (Baldrich & Domínguez, 2024). Specifically, it focuses on emerging technologies and reports on the impact of AI in the educational field; from the perspective of the Sustainable Development Goals (SDGs) of UNESCO's 2030 Agenda, it explores the opportunities AI offers for both students and faculty. Specifically, "SDG 4 highlights the use of AI technologies to ensure equitable and inclusive access to education" (Flores & García, 2023, p. 38).

However, it is worth Questioning whether educational processes in the academic sphere have fully harnessed the potential offered by technologies. If not, what has hindered this progress? Infrastructure, lack of technological resources, teacher training, student aptitude...? In the current university context, where AI tools have gained significant traction (García Peñalvo et al., 2024; López-Regalado et al., 2024), institutions cannot remain indifferent- they must take a stance and implement measures. The intersection of university education and AI technology demands it.

This study aligns with this line of research. It is part of a broader study conducted by Ibero-American scholars, which aims to analyze the use of AI in university practical training, across various degree programs in Social Sciences, Engineering, and Administrative Sciences, from a critical, ethical, and responsible perspective. The research is titled Practicum AI: Experiences of the Practicum Integrating AI (2023-2025) and is being developed within the framework of a professional knowledge community, RedTICPraxis. One of its initial objectives involves the documentary analysis of normative and ethical frameworks for the use of AI in higher education (HE).

## Ethics, Artificial Intelligence and Higher Education

AI in HE is a novel and constantly evolving issue. Research emphasizes the synergy between ethics and responsibility, as well as privacy and security, when using AI in academic contexts (Mora Naranjo et al., 2023). In this context, the need arises to generate specific policies and regulations at the corresponding institutional levels (Rodríguez-Argueta, 2020), as well as updates that integrate digital competencies and ethical awareness in study plans and programs (Vélez-Rivera et al., 2024).

The responses and reactions this technology has provoked among faculty members, specifically in their perceptions, evaluations, and positions, have been addressed in various studies. Specifically, the works by Humble and Mozelius (2022), McGrath et al. (2023), and Svaricek (2024) depict university professors who express skepticism about the benefits of AI for teaching, with concerns rooted in uncertainties about its impact on the educational context. The lack of knowledge about AI, particularly its practical applications and potential as a didactic resource (Sánchez, 2023), is contributing to faculty members feeling insecure and adopting a cautious stance, which has slowed its integration in specific areas of teaching, research, and/or university management.
On the other hand, University students are also reluctant to use AI tools due to social stigmatization and being accused of academic dishonesty. The academic environment has traditionally valued individual effort and achievement, and some students worry that using AI may be perceived as a lack of interest, convenience, lack of commitment, effort, or the authenticity of their work. Against this backdrop, experiences of integrating AI into HE curricula have begun to emerge (Gairín & Alguacil, 2024). The guidelines suggested by González Alcaide (2024) offer insights into its integration into teaching practices. The key seems to lie in knowledge and training for the effective, ethical, and responsible use of these tools (Mora Naranjo et al., 2023), building trust and encouraging their open use in the university environment, prioritizing ethical considerations when adopting AI technologies (Vélez-Rivera et al., 2024) to ensure equity, transparency, and data privacy, the goal of contributing to a more just and inclusive society.

From the second perspective, the existence of normative frameworks for AI use, research warns that in the current academic context, some HE institutions may still lack clear guidelines for the use of AI in academic work, aimed at both students and faculty members. The study by Seldon et al. (2020) highlights the impact of unregulated AI use on the academic community. Although the literature remains limited, the need for and importance of generating specific policies, regulations, and ethical norms for the responsible use of AI in educational curricula and programs are emphasized (Nguyen et al., 2023; Rodríguez-Argueta, 2020; Vélez et al., 2024), with the aim of leveraging the advantages that this tool can bring to academic and educational trajectories.

To tackle this challenge, The work of Chan (2023) can be used as a reference, which presents a political framework for ecological education in AI under three dimensions: pedagogical, governance, and operational. The author suggests exploring the integration of AI in university teaching through this ethical framework to enhance the understanding of its implications. In a similar vein, the study by Flores and García (2023, p. 45) advocates for the development of consensus-based public policy frameworks to regulate and raise awareness of the ethical use of AI in education. According to both authors:

> This must involve the design and implementation of a Digital and AI Literacy Plan, considering that the main obstacle to the widespread adoption of AI tools is the lack of solid evidence of their effectiveness and impact on students' academic achievements. Therefore, it is necessary to improve governance, accessibility, and the reliability of AI.

From the third perspective -training in digital ethics-, several considerations should be taken into account. While research highlights the critical importance of ethics and responsibility in the application of AI (Mora Naranjo et al., 2023), are students and faculty truly trained in this regard? How do they use AI? Is it critical, ethical, and responsible use? In short, are they competent in digital ethics? The reviewed studies emphasize the need to strengthen faculty training plans in digital ethics (Marín & Tur, 2024), and even assess the impact that such plans are having on students (Mora Naranjo et al., 2023); are they genuinely fostering the responsible use of AI tools? At this point, the work of Vélez-Rivera et al. (2024) underscores the need to work with the academic community, faculty, and students, in the use of these tools, in alignment with safe, ethical, and responsible academic environments.

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
In the face of this challenge, research highlights the Importance of digital ethics training for university faculty members. González Alcaide (2024) presents a model of digital literacy for teachers, while Flores and García (2023, p. 45) speak of an "AI Literacy Plan which not only trains teachers in technical skills but also in ethical and philosophical debates". On the other hand, research emphasizes the need to prepare students to face the ethical challenges associated with AI. For example, Lopezosa et al. (2023) propose a core AI training model tailored to the educational field. It includes competencies, objectives, and essential study topics on AI, and is structured to fit into a broader curriculum, for undergraduate/graduate studies or specialized seminars. In a similar vein, González Alcaide (2024) develops a didactic unit aimed at students to promote appropriate use of AI as well as optimal performance of these tools. The proposal by Aler et al. (2024) also follows a similar direction. In brief, it focuses on educational strategies, ethical competencies, and resources necessary for the trustworthy implementation of AI across various disciplines of HE. Its goal is that the next generation of young people can contribute to the use of ethical, safe, and innovative AI. Is it possible to establish fair, ethical, and sustainable educational practices in the age of AI within the academic context? Under what regulations? The AI-ethics-HE triad, through these lenses, presents an encouraging yet still underexplored landscape for study and analysis.

## Aim of the study

This study aims to analyze recent research on normative and ethical frameworks governing the use of AI in HE institutions.

## METHODOLOGY

In alignment with the stated objective, a systematic literature review was conducted through a systematic method to identify, evaluate, and interpret scientific papers (Mendoza, 2021). Systematic reviews aim to answer a well-defined research question based on the analysis and discussion of the most relevant information from the reviewed publications (Torres-Fonseca & López-Hernández, 2014). For this review, the guidelines and procedures of the PRISMA statement (*Preferred Reporting Items for Systematic reviews and Meta-Analyses*) updated in 2020 (Page et al., 2021) were used. As a first step, and in accordance with the study's objective, the following research questions were defined:

RQ1. What are the ethical challenges and risks that have been identified in universities?

RQ2. What are the normative and ethical measures that universities have implemented or proposed regarding AI?

RQ3. How is the ethical use of AI interpreted in educational processes?

RQ4. What are the didactic approaches or models that have been implemented in universities to ensure the reliable use of AI?
## Search Strategy

The literature search was conducted in the databases of Scielo, Redalyc, Scopus, Dialnet, and Web of Science. Additionally, AI tools such as Consensus were used to identify references. The search equations used to obtain significant results were: ("challenges" AND "promises of AI"); ("ethics" AND "AI" AND "higher education"); ("ethical considerations" AND "AI"); ("practicum" AND "AI").

## Inclusion and exclusion criteria for studies

The main inclusion criteria for the documents were: articles published from 2020 to the end of May 2024; in Spanish, English, or Portuguese; Studies that include empirical research at the HE level. The exclusion criteria were articles without full-text access and/or that do not narrate experiences from the ethical field applied to education.

## Process Description

The initial search identified 256 records, of which 65 were selected. Among these, 14 were duplicates and 3 were not open access, leading to their removal. The remaining documents were processed using Mendeley and Rayyan for analysis and categorization. Figure illustrates the methodological model based on the PRISMA statement.
<figure><img src="image_4.png" /><figcaption>**Figure 1**
*Flow diagram of the PRISMA model*</figcaption></figure>

In the Rayyan platform, 48 records were exported for a second stage of screening and classification of the articles. Of the total, one was a duplicate, eleven were excluded, and eight passed to a second review; the rest were retained. Additionally, eight papers from other sources of information were included, while another eight were discarded for not being in the educational field. The labels used for the classification of the articles in the Rayyan platform were ethical use, didactic models, ethical challenges and risks, academic integrity, and regulatory frameworks.

Finally, 28 articles were selected, which were processed and categorized in Excel and then analyzed based on the categories: ethical challenges and risks, normative-regulatory frameworks, ethical training, and didactic models; these same categories were considered in the qualitative analysis software MAXQDA.
## RESULTS

First, some characteristics are provided to help contextualize the set of analyzed papers. These articles are examined based on the defined categories of interest, to address the ethical aspects related to the use of AI in HE.

### Characterization of the Analyzed Articles

The synthesis of the main results is presented in Table 1 (see appendix), organized alphabetically. This table provides a summary of each study's contextual information, including reference, methodology, study location, keywords and labels.

Figure 2 illustrates the evolution of scientific production on this topic between 2020 and May 2024. The year 2023 stands out, with a notable increase in research publications, totaling 17 works, coinciding with the impact of ChatGPT's launch on November 30, 2022. In 2021 and 2024, four publications were recorded each year, while in 2022, only two works were documented, and in 2020, just one study was published.

<figure><img src="image_5.png" /><figcaption>**Figure 2**
Temporal distribution of articles</figcaption></figure>

### Geographical Distribution by Study Location

Scientific production is mainly concentrated in Spain and Ireland, which lead as the countries with the highest frequency of studies on the phenomenon, with a total of four publications each. They are followed by the Netherlands and Sweden, with two publications each. Other countries with one publication each include: Bangladesh, Canada, Chile, Croatia, China, Costa Rica, South Korea, Ecuador, Italy, Nigeria, Peru, the United Kingdom, and Switzerland. Additionally, 12 studies were identified where the geographical context was not specified

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
Methodological Design

The analysis of the methodological approach provides insight into how research on this topic is currently being conducted. According to the data presented in the Annex, bibliographic analysis studies are the most prevalent, surpassing those with quantitative, qualitative, or mixed approaches. Specifically, 17 articles (58.6%) correspond to literature reviews, whether systematic or unsystematic. The remaining 42% is distributed among mixed studies (description of an experience based on a model), with each representing 3.4%.

Approach to Categories

Through gradual immersion in the material, using the content analysis technique (Ruiz Bueno, 2021), it has been possible to identify four central issues where attention is focused in ethical and normative terms: ethical challenges and risks, normative-regulatory frameworks, ethical training, and didactic models.

RQ1. What are the ethical challenges and risks that have been detected in universities? (references on this point are shown in Figure 3).

The use of AI in teaching, learning, and assessment processes presents challenges that have been recognized in recent studies worldwide, both from the perspective of teachers and students. Its impact on HE is considerable, generating both opportunities and risks (Chan & Hu, 2023). For example, cases are presented on how university students are adapting to the use of AI in their learning (Cisneros et al., 2023). Specifically, they warn that while AI facilitates access to information and resources, it can also result in an overwhelming amount of information when there is a lack of critical skills to assess it. Even with the prevalence of AI tools that are constantly growing, students sometimes engage in academic plagiarism (Comas Forgas et al., 2023). The ease of generating content leads to this tendency and raises questions about academic integrity, as well as the originality of student productions.

<figure><img src="image_6.png" /><figcaption>**Figure 3**
References associated with ethical challenges and risks</figcaption></figure>

González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).

https://doi.org/10.5944/ried.28.2.43511
Without overlooking the opportunities offered by AI, such as the personalization of learning (Cordón García, 2023), potential risks for individuals and societies are identified. Indeed, there is talk of the "dehumanization" of education, coupled with a dependence on technology. Hence the importance of incorporating ethics into training that contemplates the use of AI in a fundamental way (Guissepe Hernández et al., 2021; Huang et al., 2021). We also find reports showing that sometimes anxiety and lack of self-efficacy in the use of AI by students (Kwak et al., 2022) can limit its adoption and effective application in specific environments. In these terms, an ethical awareness of AI is conceived, influencing the behavioral intentions of those who use it.

In summary, it is possible to emphasize that, although AI offers significant opportunities to improve teaching, learning, and assessment, it is essential to address ethical challenges, academic integrity, and the adequate preparation of students to mitigate the risks associated with its use. This also involves teachers and implies a proactive approach to training and regulating AI in educational contexts. Indeed, research reports on the topic (Ivanov, 2023; Vélez-Rivera et al., 2024) warn of the growing relevance that the AI-ethics binomial is gaining in universities, both at the general institutional level and in specific careers/areas.

### RQ2. What are the normative and ethical measures that universities have applied or suggested regarding AI?

A significant number of documents review the *need for normative frameworks and/or regulations* on the use of AI in HE (Figure 4). It has been possible to recognize numerous initiatives around normative and ethical measures that have been proposed or applied by universities around the world in recent years. The European Parliament agrees that the implementation of an Academic Ethics Code provides a normative framework that establishes principles and values with the premise of guiding conduct in relation to the use of AI, both by students and teachers (Cornejo-Plaza & Cippitani, 2023). In this sense, universities have defined acts such as plagiarism and data falsification, among other dishonest practices, as violations of this code, with established consequences for such infractions.

<figure><img src="image_7.png" /><figcaption>**Figure 4**

*References associated with regulatory and normative measures*</figcaption></figure>

González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).

https://doi.org/10.5944/ried.28.2.43511
Policies have been proposed to guide the ethical and responsible use of AI in
academic environments (Aler et al., 2024). These policies address the need to ensure
that AI technologies are used in a way that respects privacy, equity, and transparency.
Therefore, it is expected that universities establish clear rules on the use of these tools,
while providing guidelines for their integration into the educational process.

Hence, the relevance of advancing in AI training, with the implementation of
workshops and courses that address the importance of academic integrity, plagiarism,
citation, and good research practices (Cordón García, 2023). Aware of this, several
Spanish institutions have developed mandatory modules in the curriculum, for first-
year students to train them in digital ethics.

Regulatory proposals at the university level are not restricted to the teaching field, as the European Union has also established Research Ethics Committees (Piedra Alegría, 2023). The proposal is to review and approve research projects before execution to ensure ethics in data handling and the protection of participants. Yusuf et al. (2024) suggest the implementation of ethical impact assessments in projects involving AI, reviewing the social and ethical implications, which gives relevance to normative frameworks at this point.

In addition to the previous proposals, it is intended that there be regulations to generate inclusive environments based on AI, which encourage the participation of marginalized groups (Cox, 2021) through actions such as regulations for admission and scholarships to ensure equality and accessibility. Another aspect analyzed is university social responsibility and AI, so the principles of FATE (Fairness, Accountability, Transparency and Ethics) are examples of initiatives linked to academic and research activities for the well-being of the community (Henry & Oliver, 2022). All this achieved through extension and student and community collaboration projects, which encourage universities to be transparent in the use of AI algorithms and the resulting decision-making.

Data protection and privacy are another aspect to be guaranteed by institutions when incorporating AI, so regulations are aimed at ensuring that universities comply with these measures (García Peñalvo et al., 2024) through adequate management and safeguarding of information. In this regard, the evaluation of policies around the use of AI (Moya & Eaton, 2023) becomes relevant, so universities are invited to periodically review and update their regulations to support their effectiveness in the face of new challenges.

Along with the above, universities are encouraged to investigate the social, economic, environmental, and cultural repercussions of AI technologies to understand their impact on society (Chan, 2023). This synergy between research, teaching and community engagement can help institutions anticipate problems and adjust their policies based on findings.

RQ3. How is the ethical use of AI interpreted in educational processes? (Figure 5)

The findings obtained have allowed us to thoroughly understand the "ethical" implications of the use of AI in HE; this has triggered the need for a regulatory framework. Specifically, this question examines the current landscape of university policies regarding the use of AI. Of particular interest are Asian universities, which present a unique context for observing the institutional response to AI (Dai et al., 2024). It is expected that the findings will provide useful information on future directions for policy development in such universities as well as globally.

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
<figure><img src="image_8.png" /><figcaption>**Figure 5**
*References associated with the ethical use of AI in educational processes*</figcaption></figure>

Academic integrity forms an essential pillar in education, in general, and in the training of professionals, in particular, especially in a world where AI plays an increasingly significant role. Multiple tools, including the well-known ChatGPT, are present in the training of occupational therapists (Avello-Sáez & Estrada-Palavecino, 2023). In this scenario, it is worth paying attention to academic integrity to avoid undue dependencies and promote authentic learning that will be required in professional performance; among them, the ability to interpret AI decisions in line with ethical criteria (Yu & Yu, 2023). This must be worked on from the beginning of university training, with strategies that reinforce academic ethics; as warned in Social Work and Social Education degrees (Comas Forgas et al., 2023), academic plagiarism can prevail among students.

Undoubtedly, there is an inescapable responsibility in the implementation of AI at all levels of the educational system, particularly in HE (Mora Naranjo et al., 2023), where an ethical foundation, along with academic integrity is essential for its genuine acceptance and sustainability.

RQ4. What are the didactic approaches or models that have been implemented in universities for the reliable use of AI? (Figure 6)

The growing integration of AI in HE has sparked debates around didactic models and the ethical use of these technologies. The educational models that are encouraged refer to an effective integration of AI into educational practices (Chan, 2023) through specific strategies for their use in various educational contexts. This is within the framework of academic integrity standards to be defined with comprehensive policies and guidelines that are as clear as possible. Benefits for learning are recognized, while concerns revolve around the balance of autonomy/dependence, as well as originality/plagiarism.
<figure><img src="image_9.png" /><figcaption>**Figure 6**
References associated with didactic models implemented in universities</figcaption></figure>

One of the tools that has gained the most popularity in various fields, including HE and professional development of graduates, is ChatGPT. Given the importance of educating students and professionals in the proper use of these tools, guidelines are offered to promote the ethical use of this conversational AI to complement, and not replace, human competencies. This is in line with initiatives to foster a culture of academic integrity and ethical responsibility (Gallent Torres et al., 2023), particularly in the use of advanced technologies.

The concept of Education 4.0 (Gibert Delgado et al., 2023) has also been coined, referring to an approach that innovatively incorporates AI with the purpose of promoting the development of digital competencies and critical thinking in future professionals, in line with the challenges of an increasingly digitized world. Even ethics must constitute a core part of educational programs that incorporate AI, so that professionals are prepared to handle the ethical dilemmas that may arise in their work environments (Huang et al., 2021). In this context, it is expected that systems maintain high ethical standards (Aler et al., 2024), through transparent and fair evaluations, aware of the potential biases that may arise when including AI.

To summarize the relationships between the categories analyzed in this study, a co-occurrence map of codes is shown (Figure 7), which represents the ethical use of AI and its intrinsic relationship with solid normative requirements for the responsible, reliable, and transparent use of technology. The need for adequate regulations to mitigate risks is evident, through didactic models that promote a balance between technological innovation and the ethical principles that govern its use in HE.
<figure><img src="image_10.png" /><figcaption>**Figure 7**

*Co-occurrence of codes in the documents*</figcaption></figure>

**Bibliometric Analysis**

After conducting a descriptive analysis of the impact of the selected articles and with the aim of identifying possible trends in scientific production over the past five years (2020 - May 2024), a study of the relationships between their keywords was carried out. For this, the network analysis technique (Knoke & Yang, 2008) was applied, employing the VOSviewer program. From the 28 studies in the sample, 90 keywords were extracted, each with a co-occurrence index of at least two times. In the labeled relationship map (Figure 8), the nine clusters or keywords nodes are linked based on their degree of similarity, showing how they interrelate with each other.

The red cluster focuses mainly on research approaches that address the relationship between ethics and technology, especially in the fields of research involving human beings, such as education and medicine. Interest in using these technologies in educational environments is increasing, highlighting the role of digital platforms both in communicating findings and in integrating the educational community.

The green cluster focuses on the use of technology in HE to help promote inclusion at this level, especially for individuals with disabilities. It emphasizes the development of assistive technologies to improve inclusive education, facing ethical challenges in their design and accessibility to avoid inequalities.

The blue cluster highlights the challenges posed by digital transformation in the research and education sectors, underlining the role of AI as a driver of change. It

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
emphasizes the need to ensure autonomy and academic integrity, adopting a human-
centered approach.

The yellow cluster addresses the transformation that AI and big data analysis are
causing in the educational sector, particularly in optimizing teaching and learning
processes. These technologies allow for content personalization, adjusting to the
specific needs of each student, with a particular emphasis on the use of social robots to
enhance educational experience.

The purple cluster encompasses the challenges and opportunities that AI models
offer in the educational sector, with a focus on academic integrity and learning
assessment. The implementation of this technology has increased concerns related to
plagiarism, requiring the creation of innovative assessment methods that discourage
this behavior and encourage responsible use.

The turquoise cluster covers the legal field, emphasizing the role of AI in creating digitalization and automation tools that, in the educational field, offer multiple opportunities to improve accessibility and adapt teaching to individual needs. However, it also highlights the importance of assuming responsibility in the implementation of these technologies to reduce biases that could unfavorably condition learning outcomes.

The orange cluster analyzes the relationship between the use of algorithms, online
supervision, and computer systems in HE laboratories, focusing on the difficulty of
developing and using these technologies in an ethical manner.

The brown cluster refers to data and algorithm-based decision-making, also
considering possible adverse effects, such as ethical implications and biases that may
arise.

Finally, the pink cluster examines the impact of AI on access to and use of academic
resources, while addressing the adaptation and privacy challenges that come with its
implementation.

<figure><img src="image_11.png" /><figcaption>**Figure 8**
*Labeled relationship map*</figcaption></figure>

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
Considering the size of the keyword labels and the color of the different approximation zones, the relevance of the studies is observed through the density visualization map in Figure 9.

Thus, in the central zone of the map (yellow color) are, due to their importance and co-occurrence, the keywords belonging to the research line related to AI, HE, and ethics, among which are: AI, high education, ethics, online proctoring, research article, ethics research, disability, among others.

On the other hand, the peripheral zone of the map (color close to green) shows the lower density of co-occurrence of the key terms. More specific expressions are located around which the analyzed research has been focused. Words such as: academic resources, automation, digitization, privacy, students, distance-based higher education, data set, algorithm are distinguished.

<figure><img src="image_12.png" /><figcaption>**Figure 9**
Density Map</figcaption></figure>

## DISCUSSION

The purpose of this study has been to synthesize research that echoes the use of AI in the field of HE, in the face of emerging ethical challenges and/or potential risks. The studies generated in this regard shed light on how to face the challenge of balancing technological innovation with ethics, inclusion, and sustainability. Four key factors seem to be the keys: (1) overcoming ethical challenges and/or risks through the (2) design and development of regulations that ensure a (3) civic and responsible use of AI in academic contexts, and promote, in turn, the (4) implementation of ethical educational practices and/or didactic models, that encourage critical thinking both in its use and in its teaching.

However, the results obtained in this work warn of a need, with some advances, around the existing normative and ethical measures on AI, proposed by the universities

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
themselves, among other competent bodies. Although universities have been
developing regulations to guide its integration and address the most relevant
challenges (Michel-Villarreal et al., 2023) -which has meant new opportunities and
possibilities to rethink the training of future professionals (Dai et al., 2024) -, there is
still a long way to go. At this point, HE institutions are called upon to develop their
respective guidance, administration, and regulation policies for AI (UNESCO, 2023).
The existence of these normative frameworks fosters a transparent and supportive
academic environment, which educates and guides students to make appropriate and
reliable use of AI and promotes their integration into educational processes.

Regarding the design of these normative frameworks, Nguyen et al. (2023) outline
in their work seven principles that should be considered in the development of any
regulation on AI in education (AIED, as the authors refers to it): 1) governance and
administration; 2) transparency and accountability; 3) sustainability and
proportionality; 4) privacy; 5) security and protection; 6) inclusion; 7) human-centered
AIED.

Recently, in Spain, the National Institute of Educational Technologies and Teacher Training of the Ministry of Education, Vocational Training and Sports has published a guide on the use of AI in the educational field. Specifically, a decalogue is included to address the main ethical dilemmas found (Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado [INTEF], 2024): Respectful and sustainable integration; Transparency and awareness; Equity and non-discrimination; Robustness and security; Privacy and data protection; Human supervision; Human compatibility; Promotion of social well-being; Collaborative learning; Reflection and anticipation.

CONCLUSIONS

Based on the data obtained, a series of conclusions can be identified, which
respond to the research questions that have guided the work.

Regarding **RQ1**, the different studies reflect on the transition of AI adoption in HE institutions and how they face challenges such as academic integrity; it is emphasized that the unregulated use of the tool entails negative consequences such as overload or dependence, increased anxiety, lack of self-efficacy, and promotion of infractions (plagiarism, falsification). In this scenario, the design and implementation of Academic Ethics Codes are suggested, which define norms and penalize actions such as plagiarism and data alteration (Cornejo-Plaza & Cippitani, 2023), as well as the establishment of policies for the responsible use of AI, ensuring its use in environments that respect privacy, equity, and transparency (Aler et al., 2024).

Regarding **RQ2**, a trend was detected in terms of the normative and ethical
measures that universities have suggested, which align with international policies such
as those of the European Union and UNESCO (Cornejo-Plaza & Cippitani, 2023), to
adapt their possible regulations. However, the application of the regulations implies
promoting a shared responsibility between teachers and students to encourage critical
thinking in decision-making. In this regard, different institutions have implemented
courses and workshops to reinforce academic integrity, ethical use of AI, digital
competencies, good research practices (Cordón García, 2023), and the creation of
Ethics Committees (Piedra Alegría, 2023). At the same time, ethical impact
assessments have been implemented in AI projects to foresee their social and cultural
implications (Moya & Eaton, 2023; Yusuf et al., 2024) and to ensure diversity and

<footnote>González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).</footnote>

<footnote>https://doi.org/10.5944/ried.28.2.43511</footnote>
inclusion (Cox, 2021). Specific examples of ethical recommendations are those suggested by the TEC de Monterrey in Mexico (<https://bit.ly/4317udp>), where nine principles have been established for the use of AI by teachers and students. Briefly, these regulations seek to uphold academic standards and foster a culture of ethics and responsibility in universities.

Considering **RQ3**, the analyzed data indicates a leading role of universities in promoting, managing, evaluating, and updating relevant and effective frameworks, capable of anticipating problems and proposing standards. This also encompasses the field of "good practices" in research and, in correspondence with this, the integration of training in digital competencies, the creation of ethics committees, and the conduct of ethical impact assessments in AI projects. Indeed, the focus is on mitigating the risks of academic dishonesty and promoting responsible use of these tools. An example of this is found in the latest bulletin published by the National Interuniversity Council of Argentina (<https://www.cin.edu.ar/>), which deems AI to be a valid educational resource in HE.

Finally, regarding **RQ4**, it is concluded that the incorporation of AI in HE not only focuses on regulation but also on promoting appropriate use and critical reflection on its impact on different sectors of society. Thus, didactic models emerge to integrate AI into teaching practice in a way that inspires trust.

The integration of AI in HE requires not only a regulatory approach but also critical reflection on its impact on academic-professional training, knowledge creation, and the development of digital competencies. Universities are called upon to prepare to train professionals who use these technologies responsibly and collaboratively, integrating them into their didactic strategies without compromising fundamental ethical principles. This challenge implies a joint effort by the academic community to ensure that the adoption of AI is done thoughtfully, ethically, and aligned with educational quality objectives.

Through this review, the aim has been to understand the scope and nature of existing policies, as well as to identify promising practices, gaps, and challenges (Pham et al., 2014). However, this study is not without limitations. It is worth mentioning, fundamentally, the one related to the sample; most of the literature reviewed and analyzed comes from European sources, which may reflect specific cultural, regulatory, and educational frameworks, exclusive to that area. Geographical centralization -or geographical bias- can significantly affect the generalization of the findings to a broader global context. Considering this limitation, and its possible implications, it would be possible to improve the applicability of the proposals indicated above, in international contexts by conducting more studies that include diverse geographical and cultural perspectives.

In conclusion, although this study provides rigorous information on the ethical use of AI in the academic context, its limitations regarding the geographical focus of the sample require caution when generalizing the findings. Addressing these limitations, for example, through inclusive research, can help ensure that the proposals are relevant and effective in different international contexts. Nevertheless, the main challenge will be to foster a university environment that prioritizes ethical considerations and, at the same time, incorporates the latest technological advances.
REFERENCES

Aler, A., Mora, M., & Nieves, J. C. (2024). How to teach responsible AI in Higher Education: challenges and opportunities. *Ethics and Information Technology*, 26(3). https://doi.org/10.1007/s10676-023-09733-7

Avello-Sáez, D., & Estrada-Palavecino, L. (2023). ChatGPT e seu impacto na formação de competências para terapeutas ocupacionais: uma reflexão sobre a integridade acadêmica. *Cadernos Brasileiros de Terapia Ocupacional*, 31, e3534. https://doi.org/10.1590/2526-8910.ctoen271035343

Baldrich, K., & Domínguez, J. C. (2024). El uso de ChatGPT en la escritura académica: un estudio de caso en educación. *Pixel-Bit. Revista de Medios y Educación*, 71, 141-157. https://doi.org/10.12795/pixelbit.103527

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. *International Journal of Educational Technology in Higher Education*, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3

Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. *International Journal of Educational Technology in Higher Education*, 20(43). https://doi.org/10.1186/s41239-023-00411-8

Cisneros, J. D. D., Limo, F. A. F., Tinoco, L. M. B., Aybar, H. N. C., Alarcon, V. G. C., Romero, M. Á. M., & Flores, R. A. R. (2023). Adjustment of Peruvian university students to artificial intelligence. *Arts Educa*, 36, 237-248. https://artseduca.com/article-detail/?id=145

Comas Forgas, R., Cerdá-Navarro, A., Touza Garma, C., & Moreno Herrera, L. (2023). Prevalencia y factores asociados al plagio académico en estudiantes de nuevo ingreso de Trabajo Social y Educación Social: un análisis empírico. *RELIEVE. Revista Electrónica de Investigación y Evaluación Educativa*,

29(2), 1-20. https://doi.org/10.30827/relieve.v29i2.9055

Cordón García, O. (2023). Inteligencia Artificial en Educación Superior: Oportunidades y Riesgos. *RiiTE. Revista interuniversitaria de investigación en Tecnología Educativa*, (15), 16-27. https://doi.org/10.6018/riite.591581

Cornejo-Plaza, I., & Cippitani, R. (2023). Consideraciones éticas y jurídicas de la Inteligencia Artificial en Educación Superior: desafíos y perspectivas. *Revista de Educación y Derecho*, (28). https://doi.org/10.1344/REYD2023.28.4.3935

Cox, A. M. (2021). Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions. *International Journal of Educational Technology in Higher Education*, 18(3). https://doi.org/10.1186/s41239-020-00237-8

Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2024). University policies on generative AI in Asia: promising practices, gaps, and future directions. *Journal of Asian Public Policy*, 1-22. https://doi.org/10.1080/17516234.2024.2379070

Dávila Morán, R. C., & Agüero Corzo, E. C. (2023). Desafíos éticos de la inteligencia artificial: implicaciones para la sociedad y la economía. *Conrado*, 19(94), 137-144.

Flores, J. M., & García, F. J. (2023). Reflexiones sobre la ética, potencialidades y retos de la Inteligencia Artificial en el marco de la Educación de Calidad (ODS4). *Comunicar*, 31(74), 37-47. https://doi.org/10.3916/C74-2023-03

Fornasier, M. de O. (2021). Artificial intelligence and democratic rule of law. *Revista de Estudos Constitucionais, Hermenêutica e Teoria do Direito (RECHTD)*, 13(3), 351-369. https://doi.org/10.4013/rechtd.2021.133.06

Gairín, J., & Alguacil, L. (妤. (2024). La gestión de la inteligencia artificial en los contextos universitarios
iberoamericanos. EDO-Serveis
Universitat Autònoma de Barcelona.
https://ddd.uab.cat/record/300143

Gallent Torres, C., Zapata González, A., & Ortego Hernando, J. L. (2023). El impacto de la inteligencia artificial generativa en educación superior: una mirada desde la ética y la integridad académica. *RELIEVE - Revista Electrónica de Investigación y Evaluación Educativa*, 29(2). https://doi.org/10.30827/relieve.v29i2.9134

García-Fuentes, O., Raposo-Rivas, M., Martínez-Figueira, M. E., & Sarmiento-Campos, J. A. (2024). Análisis de videomanotaciones sobre el uso de recursos tecnológicos durante el Prácticum. *Pixel-Bit. Revista de Medios y Educación*, 70, 143-161.
https://doi.org/10.12795/pixelbit.102048

García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2024). The new reality of education in the face of advances in generative artificial intelligence. *RIED. Revista Iberoamericana de Educación a Distancia*, 27(1), 9-39.
https://doi.org/10.5944/ried.27.1.37716

Gibert Delgado, R. P., Gorina Sánchez, A., Reyes-Palau, N. C., Tapia-Sosa, E. V., & Siza Moposita, S. F. (2023). Educación 4.0: Enfoque innovador apoyado en la inteligencia artificial para la educación superior. *Universidad y Sociedad*, 15(6), 60-74.
https://rus.ucf.edu.cu/index.php/rus/article/view/4122/4032

González Alcaide, G. (2024). 1 d.C. (después de ChatGPT). *Inteligencia artificial generativa en la educación superior*. Publicaciones de la Universitat de València. https://puv.uv.es/1-d-c-despues-de-chatgpt.html

Guissepe Hernández, Y. K., Hernández Chávez, W. J., & Moucharrafieh Moucharrafieh, S. (2021). Orientaciones éticas en la educación superior para el uso de la inteligencia artificial en ambientes disruptivos. *REDHECS. Revista Electrónica de Humanidades, Educación y Comunicación Social*, 29(19), 69-92.
https://ojs2.urbe.edu/index.php/redhecs/article/view/4423

Henry, J. V., & Oliver, M. (2022). Who Will Watch the Watchmen? The Ethical political Arrangements of Algorithmic Proctoring for Academic Integrity. *Postdigital Science and Education*, 4(2), 330-353.
https://doi.org/10.1007/s42438-021-00273-1

Holmes, W., Iniesto, F., Anastopoulou, S., & Boticario, J. G. (2023). Stakeholder perspectives on the ethics of AI in distance-based higher education. *International Review of Research in Open and Distributed Learning*, 24(2), 96-117.
https://doi.org/10.19173/irrodl.v24i2.6089

Huang, C., Samek, T., & Shiri, A. (2021). AI and ethics: Ethical and educational perspectives for LIS. *Journal of Education for Library and Information Science*, 62(4), 351-365.
https://doi.org/10.3138/jelis-62-4-2020-0106

Humble, N., & Mozelius, P. (2022). The threat, hype, and promise of artificial intelligence in education. *Discover Artificial Intelligence*, 2(1), 22.
https://doi.org/10.1007/s44163-022-00039-z

Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado. (2024). *Guía sobre el uso de la Inteligencia Artificial en el ámbito educativo*. Ministerio de Educación, Formación Profesional y Deportes.
https://code.intef.es/wp-content/uploads/2024/07/Gu%C3%ADa-sobre-el-uso-de-la-IA-en-el-ámbito-educativo-INTEF_2024.pdf

Ivanov, S. (2023). The dark side of artificial intelligence in higher education. *The Service Industries Journal*, 43(15-16), 1055-1082.
https://doi.org/10.1080/02642069.2023.2258799

Knoke, D., & Yang, S. (2008). *Social network analysis*. Sage.
https://doi.org/10.4135/9781412985864

Kwak, Y., Ahn, J. W., & Seo, Y. H. (2022). Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students' behavioral intentions. *BMC nursing*, 21(1), 267.
https://doi.org/10.1186/s12912-022-01048-0

López-Regalado, O., Núñez-Rojas, N., López-Gil, O. R., & Sánchez-Rodríguez, J. (2024). Análisis del uso de la inteligencia artificial en la educación universitaria: una revisión sistemática. *Pixel-Bit. Revista de Medios y Educación*, 70, 97-122.

https://doi.org/10.12795/pixelbit.106336

Lopezosa, C., Codina, L., Pont-Sorribes, C., & Vállez, M. (2023). Use of generative artificial intelligence in the training of journalists: challenges, uses and training proposals. *Profesional de la información*, 32(4), e320408.

https://doi.org/10.3145/epi.2023.jul.08

Marín, V. I., & Tur, G. (2024). Ethical Issues in the Use of Technologies in Education Settings: A Scoping Review. *Education in the Knowledge Society*, 25, e31301.

https://doi.org/10.14201/eks.31301

McGrath, C., Pargman, T. C., Juth, N., & Palmgren, P. J. (2023). University teachers' perceptions of responsibility and artificial intelligence in higher education. An experimental philosophical study. *Computers and Education: Artificial Intelligence*, 4, 100139.

https://doi.org/10.1016/j.caeai.2023.100139

Mendoza, D. S. O. E. (2021). Las revisiones sistemáticas como proceso de selección artificial del conocimiento científico en el área de salud. *Revista de Ciencias Sociales*, 30(59).

https://doi.org/10.20983/noesis.2021.1.9

Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and Opportunities of Generative AI for Higher Education as Explained by ChatGPT. *Education Sciences*, 13(9), 856.

https://doi.org/10.3390/educsci13090856

Mora Naranjo, B. M., Aroca Izurieta, C. E., Tiban Leica, L. R., Sánchez Morrillo, C. F., & Jiménez Salazar, A. (2023). Ética y Responsabilidad en la implementación de la Inteligencia Artificial en la educación.

*Ciencia Latina. Revista Científica*

Multidisciplinar, 7(6), 2054-2076.

https://doi.org/10.37811/cl_rcm.v7i6.8833

Moya, B. A., & Eaton, S. E. (2023). Examinando Recomendaciones para el Uso de la Inteligencia Artificial Generativa con Integridad desde una Lente de Enseñanza y Aprendizaje. *RELIEVE. Revista Electrónica de Investigación y Evaluación Educativa*, 29(2).

https://doi.org/10.30827/relieve.v29i2.9295

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. *Education and Information Technologies*, 28(4), 4221-4241.

https://doi.org/10.1007/s10639-022-11316-w

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. *BMJ*, 372(71).

https://doi.org/10.1136/bmj.n71

Pham, M. T., Rajić, A., Greig, J. D., Sargeant, J. M., Papadopoulos, A., & McEwen, S. A. (2014). A scoping review of scoping reviews: Advancing the approach and enhancing the consistency. *Research Synthesis Methods*, 5(4), 371-385.

https://doi.org/10.1002/jrsm.1123

Piedra Alegría, J. (2023). Anotaciones iniciales para una reflexión ética sobre la regulación de la Inteligencia Artificial en la Unión Europea. *Revista de Derecho*, (28), e3264.

https://doi.org/10.22235/rd28.3264

Pierrès, O., Christen, M., Schmitt-Koopmann, F. M., & Darvishy, A. (2024). Could the use of AI in Higher Education hinder students with disabilities? A scoping review. *IEEE Access*, 12, 27810-27828.

https://doi.org/10.1109/ACCESS.2024.365368

Rodríguez-Argueta, C. M. (2020). Tendencias de la oferta en educación
superior en El Salvador - Relevancia de las carreras en Ciencia, Tecnología, Ingenierías y Matemáticas (por sus siglas en inglés STEM) ante la nueva economía digital. *Entorno*, (70), 22-32.
https://doi.org/10.5377/entorno.voi69.9559

Ruiz Bueno, A. (2021). *El contenido y su análisis: enfoque y proceso*. Universitat de Barcelona.
http://hdl.handle.net/2445/179232

Samuel, G., & Derrick, G. (2020). Defining ethical standards for the application of digital tools to population health research. *Bulletin of the World Health Organization*, 98(4), 239.
https://doi.org/10.2471/BLT.19.237370

Sánchez, M. M. (2023). La inteligencia artificial como recurso docente: usos y posibilidades para el profesorado. *Educar*, 60(1), 33-47.
https://doi.org/10.5565/rev/educar.1810

Seldon, A., Lakhani, P., & Luckin, R. (2020). *The Ethical Framework for AI in Education*. The Institute for Ethical AI in Education. https://bit.ly/3Q9yobB

Svaricek, R. (2024). Sceptical teacher and silent students: a case study of teacher changes during a teacher professional development programme. *Journal of Education for Teaching*, 50(2), 310-328.
https://doi.org/10.1080/02607476.2023.2296459

Torres-Fonseca, A., & López-Hernández, D. (2014). Criterios para publicar artículos de revisión sistemática. *Revista de Especialidades Médico-quirúrgicas*, 19(3), 393-399.

UNESCO. (2023). UNESCO survey: Less than 10% of schools and universities have formal guidance on AI. UNESCO.
https://www.unesco.org/en/articles/unesco-survey-less-10-schools-and-universities-have-formal-guidance-ai

Vélez-Rivera, R., Muñoz-Álvarez, D., Leal-Orellana, P., & Ruiz-Garrido, A. (2024). Uso de Inteligencia Artificial en educación superior y sus implicancias éticas. Mapeo sistemático de literatura. *Hachetetepé. Revista científica de Educación y Comunicación*, (28).
https://doi.org/10.25267/Hachetetepe.2024.i28.1105

Villegas, V., & Delgado, M. (2024). Inteligencia artificial: revolución educativa innovadora en la Educación Superior. *Pixel-Bit. Revista de Medios y Educación*, 71, 159-177.
https://doi.org/10.12795/pixelbit.107760

Yu, L., & Yu, Z. (2023). Qualitative and quantitative analyses of artificial intelligence ethics in education using VOSviewer and CitNetExplorer. *Frontiers in Psychology*, 14, 1061778.
https://doi.org/10.3389/fpsyg.2023.1061778

Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. *International Journal of Educational Technology in Higher Education*, 21(21).
https://doi.org/10.1186/s41239-024-00453-6

**Date of reception:** 1 December 2024

**Date of acceptance:** 7 March 2025

**Date of approval for layout:** 31 March 2025

**Date of publication in OnlineFirst:** 22 April 2025

**Date of publication:** 1 July 2025
# APPENDIX

<table><caption>**Table 1**
*List of references analyzed*</caption><thead><tr><td>Reference</td><td>Methodology</td><td>Study Location</td><td>Keywords</td><td>Tags</td></tr></thead><tbody><tr><td>Aler et al. (2024)</td><td>Literature review</td><td>Croatia. Ireland. Netherlands. Spain. Sweden. United States</td><td>Trustworthy. AI Higher Education. AI ethics. Educational strategies</td><td>Ethical use, regulatory-normative frameworks, ethical risks</td></tr><tr><td>Avello-Sáez & Estrada-Palavecino (2023)</td><td>Literature review</td><td>Not specified</td><td>IA. Formación profesional. Terapia ocupacional. Ética basada en principios. Competencia profesional. Estudiantes</td><td>Academic integrity</td></tr><tr><td>Chan (2023)</td><td>Mixed</td><td>China</td><td>AI policy framework. AI. ChatGPT. Ethics. Assessment</td><td>Ethical use, regulatory-normative frameworks, ethical risks</td></tr><tr><td>Cisneros et al. (2023)</td><td>Mixed</td><td>Peru</td><td>AI. Adaptation. Privacy. Academic Resources. Perú</td><td>Ethical challenges, risks</td></tr><tr><td>Comas et al. (2023)</td><td>Quantitative</td><td>Spain</td><td>Plagio académico. Integridad académica. Fraude en pruebas de evaluación. Educación superior</td><td>Academic integrity, ethical challenges</td></tr><tr><td>Cordón García (2023)</td><td>Literature review</td><td>Not specified</td><td>IA. Enseñanza superior. Nuevas tecnologías. Innovación pedagógica. Tecnología de la educación</td><td>Regulatory-normative frameworks, challenges</td></tr><tr><td>Cornejo-Plaza & Cippitani (2023)</td><td>Theoretical reflective</td><td>Not specified</td><td>Educación. IA. Integridad científica. Libertad de investigación. ChatGPT</td><td>Ethical challenges, normative frameworks</td></tr><tr><td>Cox (2021)</td><td>Narratives</td><td>Not specified</td><td>AI. Robots. Social robots. Learning analytics. Big data. AIEd. Design fiction</td><td>Ethical challenges, regulatory-normative frameworks</td></tr><tr><td>Dávila Morán & Agüero Corzo (2023)</td><td>Literature review</td><td>Not specified</td><td>IA. Ética. Responsabilidad. Sesgo. Automatización</td><td>Ethical challenges, risks</td></tr><tr><td>Fornasier (2021)</td><td>Literature review</td><td>Not specified</td><td>Legal education. Digitization. AI</td><td>Didactic models, academic integrity</td></tr></tbody></table>

González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).

https://doi.org/10.5944/ried.28.2.43511
<table><thead><tr><td>Reference</td><td>Methodology</td><td>Study Location</td><td>Keywords</td><td>Tags</td></tr></thead><tbody><tr><td>Gallent Torres et al. (2023)</td><td>Literature review</td><td>Not specified</td><td>Ética. Integridad académica. IAG. Educación superior</td><td>Didactic models</td></tr><tr><td>García Peñalvo et al. (2024)</td><td>Literature review</td><td>Not specified</td><td>IA. IAG. ChatGPT. Educación</td><td>Academic integrity, regulatory-normative frameworks</td></tr><tr><td>Gibert Delgado et al. (2023)</td><td>Literature review</td><td>Not specified</td><td>Educación 4.0. IA. Enseñanza personalizada. Investigación científica. Educación superior</td><td>Didactic models</td></tr><tr><td>Guissepe Hernández et al. (2021)</td><td>Literature review</td><td>Not specified</td><td>IA. Ética. ES. Ambiente Disruptivo</td><td>Ethical challenges, regulatory-normative frameworks</td></tr><tr><td>Henry & Oliver (2022)</td><td>Literature review</td><td>Not specified</td><td>Care. Ethics. Online proctoring. Higher education. Algorithms. AI</td><td>Regulatory-normative frameworks, challenges</td></tr><tr><td>Holmes et al. (2023)</td><td>Qualitative</td><td>Spain</td><td>AI. Ethics. Distance-based higher education. Students. Teachers. Institutions. Theoretical framework</td><td>Ethical use, didactic models</td></tr><tr><td>Huang et al. (2021)</td><td>Literature review</td><td>Not specified</td><td>AI. Critical LIS. Ethics. Higher education</td><td>Ethical challenges, didactic models</td></tr><tr><td>Ivanov (2023)</td><td>Literature review</td><td>Not specified</td><td>AI. Higher education. Negative impacts. Ethics. Decision-making approaches</td><td>Regulatory-normative frameworks, risks</td></tr><tr><td>Kwak et al. (2022)</td><td>Quantitative</td><td>Korea</td><td>AI ethics awareness. Attitude toward AI. Anxiety. Self-efficacy. Behavioral intention</td><td>Ethical challenges, didactic models</td></tr><tr><td>Lopezosa et al. (2023)</td><td>Qualitative</td><td>Spain</td><td>IA. Periodismo. Periodistas. Comunicación. Entrevistas. Formación. Innovación. Planes docentes. Universidad. Estudios universitarios. Competencias. Capacitación. Ética. ChatGPT</td><td>Ethical uses, academic integrity, didactic models, regulatory-normative frameworks</td></tr><tr><td>Mora Naranjo et al. (2023)</td><td>Mixed</td><td>Ecuador</td><td>IA. Ética. Responsabilidad. Educación superior</td><td>Ethical use, risks, didactic models</td></tr></tbody></table>

González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).

https://doi.org/10.5944/ried.28.2.43511
<table><thead><tr><td>Reference</td><td>Methodology</td><td>Study Location</td><td>Keywords</td><td>Tags</td></tr></thead><tbody><tr><td>Moya &amp; Eaton (2023)</td><td>Literature review</td><td>Canada</td><td>IA. IAG. Modelos de lenguaje avanzados. Integridad académica. Scholarship of teaching and learning. Enfoque de sistemas</td><td>Academic integrity, ethical challenges</td></tr><tr><td>Piedra Alegría (2023)</td><td>Literature review</td><td>Costa Rica</td><td>Ética. Regulación. Autorregulación. IA. Derechos humanos</td><td>Regulatory-normative frameworks, ethical challenges</td></tr><tr><td>Pierrès et al. (2024)</td><td>Literature review</td><td>Switzerland</td><td>AI. Disabilities. Higher education. Educational technologies. Ethics. Risk assessment</td><td>Ethical challenges, risks, didactic models, regulatory-normative frameworks</td></tr><tr><td>Samuel &amp; Derrick (2020)</td><td>Experience of a model</td><td>United Kingdom of Great Britain. Northern Ireland</td><td>Investigación sanitaria. ES</td><td>Ethical use, regulatory-normative frameworks, risks</td></tr><tr><td>Vélez-Rivera et al. (2024)</td><td>Literature review</td><td>Chile</td><td>Educación superior. Tecnologías emergentes. IA. Ética. Integridad académica</td><td>Ethical use, regulatory-normative frameworks, ethical risks and challenges</td></tr><tr><td>Yu &amp; Yu (2023)</td><td>Literature review</td><td>Italy</td><td>AI. Ethics. Bibliometric analysis. VOSviewer. CitNetExplorer</td><td>Ethical use, didactic models, academic integrity</td></tr><tr><td>Yusuf et al. (2024)</td><td>Mixed</td><td>Nigeria. Spain</td><td>GenAI. Higher education. Potential. Concerns. Ethical regulations. Cultural dimensions</td><td>Regulatory-normative frameworks, academic integrity, ethical use, didactic models</td></tr></tbody></table>

González-Fernández, M. O., Romero-López, M. A., Sgreccia, N. F., & Latorre Medina, M. J. (2025). Normative framework for ethical and trustworthy AI in higher education: state of the art. [Marcos normativos para una IA ética y confiable en la educación superior: estado de la cuestión]. *RIED-Revista Iberoamericana de Educación a Distancia*, 28(2).

https://doi.org/10.5944/ried.28.2.43511