ARTIFICIAL INTELLIGENCE (AI) IN
THE MEXICAN UNIVERSITY CONTEXT:
AN EXPLORATORY ANALYSIS FROM
THE RESEARCHER’S CONTEXT
INTELIGENCIA ARTIFICIAL (IA) EN EL
CONTEXTO UNIVERSITARIO MEXICANO:
UN ANÁLISIS EXPLORATORIO DESDE EL
CONTEXTO DEL INVESTIGADOR
Evelio Gerónimo Bautista
UPN 142 Tlaquepaque, México
Javier Gonzalo Rodríguez Ruiz
Universidad de Guadalajara, México
Erika Ochoa Rosas
UPN 142 Tlaquepaque, México
pág. 7757
DOI: https://doi.org/10.37811/cl_rcm.v9i5.20117
Artificial Intelligence (AI) in the Mexican University Context:
An Exploratory Analysis from the Researcher’s Context
Evelio Gerónimo Bautista1
gebe_bautista@hotmail.com
https://orcid.org/0000-0001-6795-0404
UPN 142 Tlaquepaque
México
Javier Gonzalo Rodríguez Ruiz
javier.rruiz@academicos.udg.mx
https://orcid.org/0000-0003-2547-5996
Universidad de Guadalajara
México
Erika Ochoa Rosas
ochoarosaserika@gmail.com
https://orcid.org/0000-0002-7489-4321
UPN 142 Tlaquepaque
México
ABSTRACT
This research examines the perceptions of Mexican researchers regarding the adoption, utilization,
familiarity, and perceived usefulness of AI tools in Mexican universities. Objective: We aimed to
explore the potential of AI tools in both teaching and research within Mexican higher education,
which is transforming teaching, learning, and research methods and practices. Methodology: The
design and implementation of a survey allowed us to gather information on expectations for using AI
in teaching and research activities. The results indicate that researchers are familiar with AI tools and
have high satisfaction with their use, particularly in personalized or collaborative learning
applications. Conclusions: This pioneering analysis for Mexico seeks to contribute to the debate on
the benefits and risks of the widespread use of AI tools in higher education institutions. At the same
time, it invites the academic and scientific communities to delve deeper into the implications of using
AI in the higher education system and high-level research, benefiting students, teaching practices, and
society as a whole.
Keywords: deep learning, teaching, research, university
1 Autor principal
Correspondencia: ochoarosaserika@gmail.com
DOI: https://doi.org/10.37811/cl_rcm.v9i5.20117
Artificial Intelligence (AI) in the Mexican University Context:
An Exploratory Analysis from the Researcher’s Context
Evelio Gerónimo Bautista1
gebe_bautista@hotmail.com
https://orcid.org/0000-0001-6795-0404
UPN 142 Tlaquepaque
México
Javier Gonzalo Rodríguez Ruiz
javier.rruiz@academicos.udg.mx
https://orcid.org/0000-0003-2547-5996
Universidad de Guadalajara
México
Erika Ochoa Rosas
ochoarosaserika@gmail.com
https://orcid.org/0000-0002-7489-4321
UPN 142 Tlaquepaque
México
ABSTRACT
This research examines the perceptions of Mexican researchers regarding the adoption, utilization,
familiarity, and perceived usefulness of AI tools in Mexican universities. Objective: We aimed to
explore the potential of AI tools in both teaching and research within Mexican higher education,
which is transforming teaching, learning, and research methods and practices. Methodology: The
design and implementation of a survey allowed us to gather information on expectations for using AI
in teaching and research activities. The results indicate that researchers are familiar with AI tools and
have high satisfaction with their use, particularly in personalized or collaborative learning
applications. Conclusions: This pioneering analysis for Mexico seeks to contribute to the debate on
the benefits and risks of the widespread use of AI tools in higher education institutions. At the same
time, it invites the academic and scientific communities to delve deeper into the implications of using
AI in the higher education system and high-level research, benefiting students, teaching practices, and
society as a whole.
Keywords: deep learning, teaching, research, university
1 Autor principal
Correspondencia: ochoarosaserika@gmail.com
pág. 7758
Inteligencia Artificial (IA) en el Contexto Universitario Mexicano: Un
Análisis Exploratorio desde el Contexto del Investigador
RESUMEN
Esta investigación explora las percepciones de los investigadores mexicanos respecto a la adopción, el
uso, la familiaridad y la utilidad de las herramientas de IA en las universidades mexicanas. Objetivo:
Se buscó investigar el potencial de las herramientas de IA tanto en la docencia como en la
investigación en la educación superior mexicana, donde la educación superior está transformando los
métodos y prácticas de enseñanza, aprendizaje e investigación. Metodología: El diseño e
implementación de una encuesta nos permitió recopilar información sobre las expectativas del uso de
la IA en las actividades de docencia e investigación. Los resultados muestran la familiaridad de los
investigadores con las herramientas de IA y una alta satisfacción con su uso, incluyendo aplicaciones
de aprendizaje personalizado o colaborativo. Conclusiones: Este análisis pionero para México busca
contribuir al debate sobre los beneficios y riesgos del uso generalizado de herramientas de IA en las
instituciones de educación superior, a la vez invita a las comunidades académicas y científicas para
profundizar en las implicaciones del uso de la IA en el sistema de educación superior y en la
investigación de alto nivel, en beneficio de los estudiantes, las prácticas docentes y la sociedad en su
conjunto.
Palabras clave: aprendizaje profundo, docencia, investigación, universidad
Artículo recibido 02 setiembre 2025
Aceptado para publicación: 29 setiembre 2025
Inteligencia Artificial (IA) en el Contexto Universitario Mexicano: Un
Análisis Exploratorio desde el Contexto del Investigador
RESUMEN
Esta investigación explora las percepciones de los investigadores mexicanos respecto a la adopción, el
uso, la familiaridad y la utilidad de las herramientas de IA en las universidades mexicanas. Objetivo:
Se buscó investigar el potencial de las herramientas de IA tanto en la docencia como en la
investigación en la educación superior mexicana, donde la educación superior está transformando los
métodos y prácticas de enseñanza, aprendizaje e investigación. Metodología: El diseño e
implementación de una encuesta nos permitió recopilar información sobre las expectativas del uso de
la IA en las actividades de docencia e investigación. Los resultados muestran la familiaridad de los
investigadores con las herramientas de IA y una alta satisfacción con su uso, incluyendo aplicaciones
de aprendizaje personalizado o colaborativo. Conclusiones: Este análisis pionero para México busca
contribuir al debate sobre los beneficios y riesgos del uso generalizado de herramientas de IA en las
instituciones de educación superior, a la vez invita a las comunidades académicas y científicas para
profundizar en las implicaciones del uso de la IA en el sistema de educación superior y en la
investigación de alto nivel, en beneficio de los estudiantes, las prácticas docentes y la sociedad en su
conjunto.
Palabras clave: aprendizaje profundo, docencia, investigación, universidad
Artículo recibido 02 setiembre 2025
Aceptado para publicación: 29 setiembre 2025
pág. 7759
INTRODUCTION
The global economy has generated multiple changes, and the incorporation of Artificial Intelligence
(AI) is gaining popularity as a general-purpose technology. Over the last decade, the concept of AI
has gained particular relevance in the productive and government sectors. We are witnessing a
revolution in the educational context, where teachers are transforming their teaching techniques and
adopting technological and pedagogical tools, thereby changing their approach to learning and
interacting with students. In this sense, the Mexican educational system is seeking the most effective
way to utilize technological tools across all types of universities and higher education institutions.
Some of the visible benefits of using these tools include personalizing the teaching-learning process,
accessing extensive digital resources on the web, and supporting research, such as writing scientific
articles, disseminating science, and connecting with other national and international scientific
communities. Similarly, people view generative AI as an enabler that facilitates the development of
human talent, promotes innovation, and advances technological development through its integration
into the labor market (ILIA, 2024).
Despite these facts, technological tools have revolutionized all sectors of the population, generating
changes in teaching and learning paradigms in the education sector. Numerous publications have
demonstrated, for instance, that ChatGPT is the most widely used AI tool in educational contexts as a
support strategy for education (Zumba et al., 2023). However, as far as we know (the tool was
launched in 2020), it has not yet been fully exploited. Furthermore, educators and institutions utilize
digital platforms and AI tools as writing assistants, personalized learning platforms, educational
chatbots, learning management systems, and data analysis tools. The truth is that we are gradually
witnessing how people understand and apply AI tools, thanks to MOOCs, large-scale training courses,
and workshops. Educators and researchers initially use all of these AI platforms and tools as a strategy
for teacher training, daily work with students, and research.
Within the specialized literature on artificial intelligence, researchers review specialized databases
such as SCOPUS using the variables 'artificial,' 'intelligence,' and 'universities,' and they find
approximately 12,376 documents published in the last five years, with English, Chinese, Spanish,
INTRODUCTION
The global economy has generated multiple changes, and the incorporation of Artificial Intelligence
(AI) is gaining popularity as a general-purpose technology. Over the last decade, the concept of AI
has gained particular relevance in the productive and government sectors. We are witnessing a
revolution in the educational context, where teachers are transforming their teaching techniques and
adopting technological and pedagogical tools, thereby changing their approach to learning and
interacting with students. In this sense, the Mexican educational system is seeking the most effective
way to utilize technological tools across all types of universities and higher education institutions.
Some of the visible benefits of using these tools include personalizing the teaching-learning process,
accessing extensive digital resources on the web, and supporting research, such as writing scientific
articles, disseminating science, and connecting with other national and international scientific
communities. Similarly, people view generative AI as an enabler that facilitates the development of
human talent, promotes innovation, and advances technological development through its integration
into the labor market (ILIA, 2024).
Despite these facts, technological tools have revolutionized all sectors of the population, generating
changes in teaching and learning paradigms in the education sector. Numerous publications have
demonstrated, for instance, that ChatGPT is the most widely used AI tool in educational contexts as a
support strategy for education (Zumba et al., 2023). However, as far as we know (the tool was
launched in 2020), it has not yet been fully exploited. Furthermore, educators and institutions utilize
digital platforms and AI tools as writing assistants, personalized learning platforms, educational
chatbots, learning management systems, and data analysis tools. The truth is that we are gradually
witnessing how people understand and apply AI tools, thanks to MOOCs, large-scale training courses,
and workshops. Educators and researchers initially use all of these AI platforms and tools as a strategy
for teacher training, daily work with students, and research.
Within the specialized literature on artificial intelligence, researchers review specialized databases
such as SCOPUS using the variables 'artificial,' 'intelligence,' and 'universities,' and they find
approximately 12,376 documents published in the last five years, with English, Chinese, Spanish,
pág. 7760
Russian, and German as the predominant languages. This means that researchers worldwide have
published a significant amount of literature explaining the use of this tool in the educational context.
Undoubtedly, there are lessons, obstacles, and challenges in the use of AI in HEIs, such as risks on the
web, managing and utilizing large amounts of information, digital literacy, and technological
infrastructure. Michel et al. (2023) analyze the benefits and limitations of using ChatGPT in higher
education. They address concerns related to academic integrity, plagiarism detection, and the potential
impact on critical thinking skills, and highlight the need for empirical research to understand users'
experiences and perceptions.
To contextualize the importance of AI, a report prepared for Latin American countries (ILIA, 2024)
showed that Mexico was average in its AI literacy index (57.8 points), below nations such as Chile,
Uruguay, Brazil, Argentina, and Costa Rica, among others, while it ranked below Chile, Uruguay, and
Costa Rica in the human talent sub dimension. The report highlights a number of key requirements for
countries to achieve increased labor productivity and improved quality of life: early computer science
education, the inclusion of AI computational content in the curriculum, and English language skills.
Given this situation, it is urgent to generate empirical evidence on the perceptions of direct users of AI
tools, particularly in the field of high-level scientific research. Therefore, the objective of this article
was to investigate the use of AI by Mexican researchers registered in the National System of
Researchers of the Ministry of Science, Humanities, Technology, and Innovation (SECIHTI). Their
level of familiarity with the tools' everyday use, the main platforms used, the frequency of use, and
their perception of their usefulness in their daily work activities. To this end, we designed a brief
survey with thirteen items and validated it through a prior pilot test.
Based on the elements presented, we structured the article as follows: the next section addresses
relevant aspects of the literature on AI, the third section presents the methodological aspects we used,
and the fourth section presents the results. We present the conclusions and implications of our
findings and the bibliography below.
LITERATURE REVIEW
Artificial Intelligence (AI) in education approaches from different angles and perspectives, such as the
approach of pedagogy, technology, education and among others, like Computer science.
Russian, and German as the predominant languages. This means that researchers worldwide have
published a significant amount of literature explaining the use of this tool in the educational context.
Undoubtedly, there are lessons, obstacles, and challenges in the use of AI in HEIs, such as risks on the
web, managing and utilizing large amounts of information, digital literacy, and technological
infrastructure. Michel et al. (2023) analyze the benefits and limitations of using ChatGPT in higher
education. They address concerns related to academic integrity, plagiarism detection, and the potential
impact on critical thinking skills, and highlight the need for empirical research to understand users'
experiences and perceptions.
To contextualize the importance of AI, a report prepared for Latin American countries (ILIA, 2024)
showed that Mexico was average in its AI literacy index (57.8 points), below nations such as Chile,
Uruguay, Brazil, Argentina, and Costa Rica, among others, while it ranked below Chile, Uruguay, and
Costa Rica in the human talent sub dimension. The report highlights a number of key requirements for
countries to achieve increased labor productivity and improved quality of life: early computer science
education, the inclusion of AI computational content in the curriculum, and English language skills.
Given this situation, it is urgent to generate empirical evidence on the perceptions of direct users of AI
tools, particularly in the field of high-level scientific research. Therefore, the objective of this article
was to investigate the use of AI by Mexican researchers registered in the National System of
Researchers of the Ministry of Science, Humanities, Technology, and Innovation (SECIHTI). Their
level of familiarity with the tools' everyday use, the main platforms used, the frequency of use, and
their perception of their usefulness in their daily work activities. To this end, we designed a brief
survey with thirteen items and validated it through a prior pilot test.
Based on the elements presented, we structured the article as follows: the next section addresses
relevant aspects of the literature on AI, the third section presents the methodological aspects we used,
and the fourth section presents the results. We present the conclusions and implications of our
findings and the bibliography below.
LITERATURE REVIEW
Artificial Intelligence (AI) in education approaches from different angles and perspectives, such as the
approach of pedagogy, technology, education and among others, like Computer science.
pág. 7761
This section addresses the leading theorists of AI in education and its relevant advances in the
educational context, to frame the objective of this article.
Skinner (2016) introduces interesting elements about the use of technology in education. Although he
did not work directly with AI, he had an innovative vision for transforming education, adjusting both
the pace of teaching and the thematic content of the curriculum. His main contribution was the
"teaching machines", a device that used operant conditioning to obtain immediate and controlled
feedback. These machines were used to grade students' assignments and functioned as intelligent
tutoring platforms. Translated into the present, they were tools similar to adaptive learning platforms
such as Coursera, Khan Academy, Duolingo, Moodle, or Blackboard, to name a few, with the
significant differentiating effect of the disruptive technological era in which we find ourselves.
Along the same lines, Turing (2021), in addition to being the father of computer science and AI, set a
precedent in the design of a test since 1950. It was an experiment that sought to measure whether
machines thought, imitating humans. This test stood out for both approximating the measurement of
the machine's deep intelligence and its ability to simulate it (in terms of reasoning, learning, and
conversation). It was a disruptive and innovative proposal, as it revolutionized the philosophy of the
mind and intelligence in the field of computer science.
In line with the theme, Marvin Minsky and John McCarthy (1927–2011), who designed machines to
simulate cognitive processes, made the first contributions to AI. The relevance of these contributions
lay in machine theories and the LISP (List Processing) programming language, which laid the
foundation for AI, crucial advances in education. Minsky (1988) proposed in his theory the "society
of the mind," a composite society where all elements interact with each other to generate a more
complex process. He also contributed to the development of fundamental algorithms for AI, including
automatic processing and logical reasoning systems. Other contributions by Minsky were "artificial
neural networks." Together with Dean Edmond, he created the first neural network simulator, the
Stochastic Neural Analog Reinforcement Calculator (SNARC), in 1951, and his criticisms helped to
promote the approach of deep learning (the purpose of the neural network was to imitate a rat learning
to get out of a maze).
This section addresses the leading theorists of AI in education and its relevant advances in the
educational context, to frame the objective of this article.
Skinner (2016) introduces interesting elements about the use of technology in education. Although he
did not work directly with AI, he had an innovative vision for transforming education, adjusting both
the pace of teaching and the thematic content of the curriculum. His main contribution was the
"teaching machines", a device that used operant conditioning to obtain immediate and controlled
feedback. These machines were used to grade students' assignments and functioned as intelligent
tutoring platforms. Translated into the present, they were tools similar to adaptive learning platforms
such as Coursera, Khan Academy, Duolingo, Moodle, or Blackboard, to name a few, with the
significant differentiating effect of the disruptive technological era in which we find ourselves.
Along the same lines, Turing (2021), in addition to being the father of computer science and AI, set a
precedent in the design of a test since 1950. It was an experiment that sought to measure whether
machines thought, imitating humans. This test stood out for both approximating the measurement of
the machine's deep intelligence and its ability to simulate it (in terms of reasoning, learning, and
conversation). It was a disruptive and innovative proposal, as it revolutionized the philosophy of the
mind and intelligence in the field of computer science.
In line with the theme, Marvin Minsky and John McCarthy (1927–2011), who designed machines to
simulate cognitive processes, made the first contributions to AI. The relevance of these contributions
lay in machine theories and the LISP (List Processing) programming language, which laid the
foundation for AI, crucial advances in education. Minsky (1988) proposed in his theory the "society
of the mind," a composite society where all elements interact with each other to generate a more
complex process. He also contributed to the development of fundamental algorithms for AI, including
automatic processing and logical reasoning systems. Other contributions by Minsky were "artificial
neural networks." Together with Dean Edmond, he created the first neural network simulator, the
Stochastic Neural Analog Reinforcement Calculator (SNARC), in 1951, and his criticisms helped to
promote the approach of deep learning (the purpose of the neural network was to imitate a rat learning
to get out of a maze).
pág. 7762
McCarthy (1960) was another of the leading theorists and creators of AI, who coined the term
"Artificial Intelligence." His main proposal was to investigate machines capable of reasoning,
learning, and solving problems using a programming language (LISP). Furthermore, the author raised
another interesting point: "Time-Sharing Systems," a method for sharing computer resources with
different users who needed them, a precursor to what we now know as cloud computing.
Both Minsky and McCarthy have played important roles in shaping and evaluating AI, from the
modular vision to the application of neural networks and robotics using linguistic tools. However, the
application of AI has evolved, such as the development of LOGO, constructionist learning, the use of
technology in education, learning for the future, among others. For example, Papert (1980), a
mathematician and pioneer in the use of technology applied to education, whose work has focused on
computer literacy.
In 1967, developers designed the LOGO language for educational purposes, using commands to
facilitate the learning of a high-level programming language among children and young people. This
language has enabled the learning of concepts oriented toward active and creative exploration, as well
as constructive learning, with which students construct their own knowledge. It is a type of
constructivist learning—previously proposed by Piaget—where students learn by constructing
different tangible objects (Papert, 1980). Other contributions included the automation of tasks and the
generation of personalized and meaningful learning environments, which inspired the development of
interactive digital educational platforms and multidisciplinary approaches such as the STEAM
methodology. Over time, technology has proven to transform teachers' teaching methods and student
learning, from a constructivist perspective.
In Europe, policymakers and educators approach AI from a different perspective. They refer to it as
the "digitalization of education," often without fully considering the participation of key stakeholders
in the teaching-learning process—such as school principals, parents, school boards, and legislators—
because they do not subject it to standardized, automated, or technology-enhanced assessment.
In contrast, schools must offer an appropriate education in a ubiquitously digitalized, complex, and
changing world; for example, the development of innovation would be enhanced in a context where
McCarthy (1960) was another of the leading theorists and creators of AI, who coined the term
"Artificial Intelligence." His main proposal was to investigate machines capable of reasoning,
learning, and solving problems using a programming language (LISP). Furthermore, the author raised
another interesting point: "Time-Sharing Systems," a method for sharing computer resources with
different users who needed them, a precursor to what we now know as cloud computing.
Both Minsky and McCarthy have played important roles in shaping and evaluating AI, from the
modular vision to the application of neural networks and robotics using linguistic tools. However, the
application of AI has evolved, such as the development of LOGO, constructionist learning, the use of
technology in education, learning for the future, among others. For example, Papert (1980), a
mathematician and pioneer in the use of technology applied to education, whose work has focused on
computer literacy.
In 1967, developers designed the LOGO language for educational purposes, using commands to
facilitate the learning of a high-level programming language among children and young people. This
language has enabled the learning of concepts oriented toward active and creative exploration, as well
as constructive learning, with which students construct their own knowledge. It is a type of
constructivist learning—previously proposed by Piaget—where students learn by constructing
different tangible objects (Papert, 1980). Other contributions included the automation of tasks and the
generation of personalized and meaningful learning environments, which inspired the development of
interactive digital educational platforms and multidisciplinary approaches such as the STEAM
methodology. Over time, technology has proven to transform teachers' teaching methods and student
learning, from a constructivist perspective.
In Europe, policymakers and educators approach AI from a different perspective. They refer to it as
the "digitalization of education," often without fully considering the participation of key stakeholders
in the teaching-learning process—such as school principals, parents, school boards, and legislators—
because they do not subject it to standardized, automated, or technology-enhanced assessment.
In contrast, schools must offer an appropriate education in a ubiquitously digitalized, complex, and
changing world; for example, the development of innovation would be enhanced in a context where
pág. 7763
today's student (future worker) develops critical thinking, problem-solving, communication, and
teamwork (and the use of AI systems) (Benvenuti et al., 2023, p. 2).
The European Union has implemented guidelines and resources to help people build confidence with
AI tools in educational contexts. Cortez et al. (2024) find a significant increase in the use of AI tools,
which improves student performance by providing fast and quality communication, allowing students
to interact and feel closer to both their classmates and teachers. That is, there is a connection between
teachers and students in the teaching-learning process; they also show responsibility, vigilance, and
security in the use of AI.
In the Mexican context, Sánchez (2023) explains how the use of ICT transformed society, especially
the educational sector. MOOCs gained popularity, providing students with access to information and
teaching materials from prestigious universities worldwide. The advent of AI has opened up
innovative possibilities for acquiring new knowledge, accompanied by Machine Learning, which
focuses on algorithms and systems. AI has made it possible to automate learning, reduce time with a
behaviorist and constructivist approach, as well as interactive and engaging activities.
Undoubtedly, the cases of Perplexity AI, ChatGPT, Bard, and recently Deep Seek, as well as writing
tools such as Grammarly and Gemini, have been among the most widely used and innovative
disruptive apps in the Mexican context, which is constantly evolving and being assimilated (INCyTU,
2018).
These tools influence learning processes and allow educators to assess whether AI platforms truly
support the pedagogical aspects of the classroom. Although most teachers use ChatGPT only as a
complementary tool—often without supervision or monitoring—it undoubtedly enhances the learning
process and knowledge acquisition in educational settings (Ali et al., 2024).
Reflecting on the implications observed in the Mexican case, they highlight the risks posed by the
indiscriminate and intensive use of large linguistic models, such as those introduced by ChatGPT.
Shumailov et al. (2024) go further, coining the concept of "model collapse," which they define as
degenerative, caused by learned recursive models where the data generated ends up contaminating the
possible results of the next generation, altering the results of reality in subsequent exercises.
today's student (future worker) develops critical thinking, problem-solving, communication, and
teamwork (and the use of AI systems) (Benvenuti et al., 2023, p. 2).
The European Union has implemented guidelines and resources to help people build confidence with
AI tools in educational contexts. Cortez et al. (2024) find a significant increase in the use of AI tools,
which improves student performance by providing fast and quality communication, allowing students
to interact and feel closer to both their classmates and teachers. That is, there is a connection between
teachers and students in the teaching-learning process; they also show responsibility, vigilance, and
security in the use of AI.
In the Mexican context, Sánchez (2023) explains how the use of ICT transformed society, especially
the educational sector. MOOCs gained popularity, providing students with access to information and
teaching materials from prestigious universities worldwide. The advent of AI has opened up
innovative possibilities for acquiring new knowledge, accompanied by Machine Learning, which
focuses on algorithms and systems. AI has made it possible to automate learning, reduce time with a
behaviorist and constructivist approach, as well as interactive and engaging activities.
Undoubtedly, the cases of Perplexity AI, ChatGPT, Bard, and recently Deep Seek, as well as writing
tools such as Grammarly and Gemini, have been among the most widely used and innovative
disruptive apps in the Mexican context, which is constantly evolving and being assimilated (INCyTU,
2018).
These tools influence learning processes and allow educators to assess whether AI platforms truly
support the pedagogical aspects of the classroom. Although most teachers use ChatGPT only as a
complementary tool—often without supervision or monitoring—it undoubtedly enhances the learning
process and knowledge acquisition in educational settings (Ali et al., 2024).
Reflecting on the implications observed in the Mexican case, they highlight the risks posed by the
indiscriminate and intensive use of large linguistic models, such as those introduced by ChatGPT.
Shumailov et al. (2024) go further, coining the concept of "model collapse," which they define as
degenerative, caused by learned recursive models where the data generated ends up contaminating the
possible results of the next generation, altering the results of reality in subsequent exercises.
pág. 7764
A recent work for Mexico (Huerta & Zavala, 2023), in the context of teaching, also highlighted the
lack and need for regulation and legislation in the use of AI, from a didactic and pedagogical
perspective, considering students, teachers, and institutions. Tramallino and Marize (2024) also
explain the importance of countries regulating the use of AI.
It is striking that a study on the penetration of AI in the northern Mexican state of Sinaloa (García,
2023) is notable. Revealed that the ChatGPT tool was not widely used by the student community eight
months after its market launch, while the work of Onofre et al. (2024) speaks of the integration of AI
and ChatGPT as a key tool for educational innovation. In other words, the dizzying advancement in
the use of technology in the educational field is evident, as is its impact on social and economic
progress. However, some studies for Mexico are inconclusive regarding its adoption and use.
However, research on the use of AI among academic researchers in Mexico is still scarce. These
professors operate differently from professors who interact with students. Therefore, this article
explores researchers' approach to AI tools in their daily work, highlighting their potential to improve
efficiency and productivity, both in the classroom and in their research at the highest levels.
METHODOLOGY
We conducted a descriptive analysis based on cross-sectional data. The study included only research
professors registered in Mexico's National System of Researchers (SNI) of the Secretariat of Science,
Technology, and Innovation (SECITI) who were members of an instant messaging group. During the
last week of September 2024, we recruited participants over a seven-day period, and they completed
and validated 62 surveys.
We focused the survey on the use of AI in the educational context, both in teaching and research.
Higher education institutions from 23 of the 32 Mexican states contributed to this sample. To better
understand how teachers integrate AI tools into their activities, we organized participants into age
groups. We used an exploratory approach to present the main findings and constructed tables, graphs,
and word clouds. We designed the 13-item survey to gather basic sociodemographic data and insights
into the penetration of AI in tertiary education.
It is essential to clarify that, due to the sample size; the results do not represent the entire population
of research professors in Mexico.
A recent work for Mexico (Huerta & Zavala, 2023), in the context of teaching, also highlighted the
lack and need for regulation and legislation in the use of AI, from a didactic and pedagogical
perspective, considering students, teachers, and institutions. Tramallino and Marize (2024) also
explain the importance of countries regulating the use of AI.
It is striking that a study on the penetration of AI in the northern Mexican state of Sinaloa (García,
2023) is notable. Revealed that the ChatGPT tool was not widely used by the student community eight
months after its market launch, while the work of Onofre et al. (2024) speaks of the integration of AI
and ChatGPT as a key tool for educational innovation. In other words, the dizzying advancement in
the use of technology in the educational field is evident, as is its impact on social and economic
progress. However, some studies for Mexico are inconclusive regarding its adoption and use.
However, research on the use of AI among academic researchers in Mexico is still scarce. These
professors operate differently from professors who interact with students. Therefore, this article
explores researchers' approach to AI tools in their daily work, highlighting their potential to improve
efficiency and productivity, both in the classroom and in their research at the highest levels.
METHODOLOGY
We conducted a descriptive analysis based on cross-sectional data. The study included only research
professors registered in Mexico's National System of Researchers (SNI) of the Secretariat of Science,
Technology, and Innovation (SECITI) who were members of an instant messaging group. During the
last week of September 2024, we recruited participants over a seven-day period, and they completed
and validated 62 surveys.
We focused the survey on the use of AI in the educational context, both in teaching and research.
Higher education institutions from 23 of the 32 Mexican states contributed to this sample. To better
understand how teachers integrate AI tools into their activities, we organized participants into age
groups. We used an exploratory approach to present the main findings and constructed tables, graphs,
and word clouds. We designed the 13-item survey to gather basic sociodemographic data and insights
into the penetration of AI in tertiary education.
It is essential to clarify that, due to the sample size; the results do not represent the entire population
of research professors in Mexico.