Student perceptions on the use of AI and biometric
analysis in university neuroeducation processes
Percepción estudiantil sobre el uso de la IA y el análisis biométrico
en los procesos de neuroeducación universitaria
Received: 30/04/2026 - Accepted: 22/07/2026
Wilson Angel Gutierrez Rodriguez
https://orcid.org/0000-0001-8188-1441
dicytfactec@gmail.com
Universidad Técnica de Oruro. Oruro, Bolivia
Marianela León Mendoza
https://orcid.org/0009-0009-2050-7662
leonmendozamarianela2@gmail.com
Universidad Técnica de Oruro. Oruro, Bolivia
Graciela Mamani Torres
https://orcid.org/0009-0004-6155-2164
docentetutor2020@gmail.com
Universidad San Francisco Xavier. Sucre, Bolivia
Pamela Navarro Escobar
https://orcid.org/0009-0009-4545-9812
navarro.docentetutor@gmail.com
Universidad San Francisco Xavier. Sucre, Bolivia
Abstract
University neuroeducation, strengthened by artificial intelligence and biometric analysis of students, has become
an innovative strategy for enhancing teaching and learning processes in higher education. The general objective of
this research was to analyze students' perceptions of the use of artificial intelligence (AI) and biometric analysis in
university neuroeducation processes. The study was conducted using a quantitative approach, with a non-
experimental, cross-sectional, and descriptive-correlational design. The sample consisted of 120 university
students who responded to a structured survey based on a Likert-type scale. The instrument was validated by a
panel of 10 specialists in education, technology, and scientific research. The results showed that the majority of
students perceived the use of AI and educational biometrics favorably, highlighting benefits associated with
personalized learning, increased motivation, and improved academic performance. However, concerns related to
privacy and the ethical management of biometric data were also identified. It is concluded that these technologies
represent an innovative alternative to strengthen the quality of university teaching through more adaptive,
humanized processes supported by scientific evidence.
Keywords: neuroeducation, artificial intelligence, educational biometrics, higher education.
Resumen
La neuroeducación universitaria, fortalecida por la inteligencia artificial y el análisis biométrico del estudiantado, se
ha consolidado como una estrategia innovadora para potenciar los procesos de enseñanza y aprendizaje en la
educación superior. El objetivo general de esta investigación fue analizar la percepción de los estudiantes sobre el
uso de la inteligencia artificial (IA) y del análisis biométrico en los procesos de neuroeducación universitaria. El
estudio se desarrolló bajo un enfoque cuantitativo, con un diseño no experimental, transversal y descriptivo-
correlacional. La muestra estuvo conformada por 120 estudiantes universitarios, quienes respondieron una
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encuesta estructurada basada en una escala tipo Likert. El instrumento fue validado por un panel de 10
especialistas en educación, tecnología e investigación científica. Los resultados evidenciaron que la mayoría de
los estudiantes percibió favorablemente el uso de la IA y la biometría educativa, destacando beneficios asociados
con la personalización del aprendizaje, el incremento de la motivación y la mejora del rendimiento académico. No
obstante, también se identificaron preocupaciones relacionadas con la privacidad y la gestión ética de los datos
biométricos. Se concluye que estas tecnologías representan una alternativa innovadora para fortalecer la calidad
de la enseñanza universitaria mediante procesos más adaptativos, humanizados y sustentados en evidencia
científica.
Palabras clave: neuroeducación, inteligencia artificial, biometría educativa, educación superior.
Introduction
University neuroeducation, enhanced by artificial intelligence and student biometric analysis, currently
represents one of the most important emerging fields within the digital transformation of contemporary higher
education. This area of research integrates knowledge derived from cognitive neuroscience, artificial intelligence,
learning analytics, and educational biometrics to achieve a deeper understanding of how university students learn
in complex technological environments. According to Luckin (2016), artificial intelligence applied to education
promotes personalized learning experiences through algorithms capable of interpreting academic and cognitive
behaviors. Similarly, Baker et al. (2021) argue that intelligent technologies strengthen adaptive educational
processes and improve interactions among students, teachers, and university digital platforms.
In the Latin American context, the incorporation of artificial intelligence in higher education institutions has
increased considerably in recent years due to the accelerated educational digitalization that followed the pandemic.
In this regard, Bolivia has not been left out of this transformation, as several universities have begun implementing
smart platforms, automated tutoring systems, and biometric tools focused on academic monitoring. According to
Cabero-Almenara et al. (2025), higher education institutions face the challenge of integrating emerging
technologies while maintaining a balance between innovation, ethics, and pedagogical quality. However, Salas-
Pilco et al. (2022) state that Latin American universities still experience limitations related to technological
infrastructure, digital literacy, and the protection of biometric data in contemporary educational processes.
The relevance of this topic lies in the fact that university neuroeducation allows for a scientific understanding
of the cognitive and emotional mechanisms involved in human learning in digital environments. The integration of
artificial intelligence and biometrics enables the identification of patterns associated with attention, motivation,
academic stress, and student performance through advanced technological tools. According to D’Mello and
Graesser (2014), biometric systems can detect emotions and cognitive states relevant to optimizing university
educational experiences. Likewise, Bond et al. (2020) argue that higher education requires pedagogical models
based on neuroscientific evidence, capable of strengthening retention, participation, and academic success in
hybrid and virtual learning contexts. Furthermore, this topic has gained social relevance due to the problems
associated with university dropout rates, low academic participation, and limited educational personalization in
Bolivian higher education institutions. In this context, artificial intelligence and biometric analysis could contribute
to the early detection of academic and emotional risk factors that affect student performance. According to Holmes
et al. (2019), smart technologies have predictive capabilities to identify patterns of academic failure through the
automated analysis of behavioral and cognitive data. Furthermore, they strengthen institutional decision-making
processes aimed at improving the university experience and student well-being.
However, it is crucial to understand that the use of artificial intelligence and biometrics in university settings
also presents a significant scientific and ethical challenge due to the implications associated with privacy, digital
surveillance, and the management of sensitive personal data. In Bolivia, many institutions still lack clear policies
regulating the pedagogical and ethical use of students’ biometric information.
From a pedagogical perspective, another relevant issue concerns the limited teacher training needed to
adequately interpret the biometric data generated by intelligent platforms and transform this information into
effective teaching strategies. While emerging technologies offer significant analytical opportunities, many university
professors still lack the advanced digital skills necessary to integrate these tools into their academic practices.
According to Redecker (2017), the digital transformation of education requires strengthening techno-pedagogical
competencies related to the critical and reflective use of complex educational data.
Consequently, this research is developed from the theoretical perspective of connectivism and applied
neuroeducation, approaches that explain learning as a dynamic process supported by interconnected cognitive,
emotional, and technological networks. Connectivism posits that knowledge is constructed through digital
interactions mediated by intelligent technologies and collaborative virtual environments. According to Downes
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(2019), contemporary learning depends on the ability to establish meaningful connections between information,
experiences, and distributed digital resources.
From this perspective, the fundamental principles of the selected theory are associated with brain plasticity,
adaptive learning, and the interaction between emotion, cognition, and educational technology. Neuroeducation
maintains that learning improves when educational experiences consider neurophysiological and emotional factors
that affect university students. According to Immordino-Yang and Damasio (2021), emotions play an essential role
in consolidating meaningful learning and in complex cognitive decision-making processes. Similarly, Cuéllar et al.
(2025) state that educational artificial intelligence can strengthen academic personalization through biometric
analyses designed to detect levels of attention, motivation, and cognitive load during university learning processes.
Regarding previous research, several international studies have addressed the application of artificial
intelligence in higher education from neuroscientific, technological, and analytical perspectives. These studies have
demonstrated that educational biometrics allows for the collection of relevant information about student
engagement, emotions, and academic behavior. According to Chen et al. (2022), facial recognition and the
automated analysis of emotional expressions, for example, contribute significantly to understanding the affective
states associated with university learning. Similarly, Giannakos et al. (2019) point out that multimodal artificial
intelligence systems facilitate more accurate pedagogical feedback processes through the use of biometric and
behavioral data.
Likewise, in Latin America, there has been growing scientific interest in the implementation of intelligent
technologies aimed at strengthening university learning processes. Several studies conducted in Mexico, Colombia,
and Chile have analyzed the impact of artificial intelligence on academic performance, student retention, and
educational adaptation. According to Ramírez-Montoya et al. (2023), Latin American universities are progressively
moving toward intelligent education models supported by big data, learning analytics, and educational artificial
intelligence. In line with this perspective, Area-Moreira and Adell-Segura (2021) state that the digital transformation
of universities requires interdisciplinary approaches capable of integrating technological innovation, human well-
being, and comprehensive cognitive development.
In summary, previous studies have demonstrated that educational artificial intelligence has great potential
to strengthen personalized and predictive teaching and learning processes in contemporary higher education.
Furthermore, the aforementioned research highlighted that biometric analysis allows for the understanding of
invisible variables of human learning that traditionally could not be measured using conventional methodologies.
According to Giannakos et al. (2019), biometric sensors applied in educational contexts allow for the identification
of attentional fluctuations and levels of cognitive engagement during virtual academic activities.
On the other hand, it is important to note that the scientific literature has also revealed ethical concerns
associated with the widespread use of biometric data in university educational institutions. While intelligent
technologies offer significant advantages for learning analysis, as has been demonstrated, several researchers
have warned about the risks related to privacy, informed consent, and the digital surveillance of students. According
to Prinsloo and Slade (2017), educational analytics must be based on transparent ethical principles that protect the
rights and freedoms of university students. Similarly, Knox et al. (2019) argue that universities need to develop
robust regulatory frameworks focused on the responsible use of algorithms and biometric technologies in digital
educational environments.
Based on this background, the present study seeks to answer the following question: How do students
perceive the use of artificial intelligence and biometric analysis in university neuroeducation processes within the
context of higher education? This question arises from the need to understand the extent to which intelligent
technologies contribute to strengthening personalized, inclusive, and neuroscience-based educational experiences.
In this sense, the general objective of the research is to analyze students' perceptions of the use of artificial
intelligence and biometric analysis in university neuroeducation processes within the context of Bolivian higher
education, identifying their pedagogical, neurocognitive, and ethical implications for academic performance.
Therefore, the research hypothesizes that students' perceptions of the use of artificial intelligence and
biometric analysis in university neuroeducation processes are favorable within the context of Bolivian higher
education. Specifically, it is proposed that personalized learning, based on neurophysiological data and predictive
algorithms, enhances student attention, motivation, and participation throughout the educational process.
Furthermore, it is argued that the ethical and pedagogically sound integration of biometric technologies strengthens
faculty decision-making aimed at reducing academic dropout rates and improving the quality of university education.
Methodology
This research was conducted using an empirical-quantitative approach, as its purpose was to analyze
students' perceptions of the use of artificial intelligence and biometric analysis in university neuroeducation
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Highly Adequate Adequate Fair Deficient
Clarity Relevance Coherence
processes within the context of higher education. This was achieved through the collection and analysis of objective
data obtained directly from the study population. The design was non-experimental and cross-sectional, since the
variables were observed in their natural context, without deliberate manipulation by the researchers. Furthermore,
the scope of the study was descriptive-correlational, which allowed for the identification of the main characteristics
of the analyzed phenomenon and the establishment of the relationship between the use of artificial intelligence
tools, biometric analysis, and university learning processes. According to Hernández-Sampieri and Mendoza
(2018), quantitative studies allow for the measurement of educational phenomena through systematic procedures
aimed at obtaining verifiable results.
The population consisted of 120 university students from higher education institutions in Bolivia, selected
through convenience sampling due to their accessibility and willingness to participate in the research process. The
participants belonged to different university programs associated with digital and technological environments,
considering inclusion criteria related to the frequent use of virtual learning platforms and educational technologies.
Since the population was relatively small and accessible, the entire population was included in the study; therefore,
no sampling formula was required. This decision made it possible to obtain a comprehensive overview of the
behavior of the analyzed variables within the selected academic context.
For data collection, the survey technique was used due to its ability to obtain direct information about
students' perceptions, experiences, and evaluations regarding the use of artificial intelligence and educational
biometrics. A structured questionnaire with closed-ended questions was designed as the research instrument,
based on a five-point Likert scale with the following response options: “strongly disagree,” “disagree,” “neutral,”
“agree,” and “strongly agree.” The instrument was organized into dimensions related to neuroeducation, educational
artificial intelligence, technological perception, and student biometric analysis. Furthermore, the items were written
in technical but accessible language to ensure their adequate comprehension by the participating university
students.
Regarding instrument validation, this process was carried out through the evaluation of 10 specialists with
experience in higher education, neuroeducation, artificial intelligence, and research methodology. The objective of
this process was to assess the clarity, relevance, and coherence of the items included in the questionnaire
administered to the university students. The experts analyzed each question, considering its relationship to the
research objectives and the thematic consistency of the dimensions being evaluated. The results obtained allowed
for the identification of a favorable level of acceptance and validity of the instrument designed for the study.
Figure 1
General results of instrument validation according to specialist criteria
Note. The evaluations reflected adequate methodological consistency and thematic alignment
between instrument items and research objectives / Source. Prepared by the authors
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The analysis of the results showed that the specialists issued highly favorable evaluations regarding the
methodological quality of the instrument designed for the study. The criteria of relevance and coherence received
the highest number of “highly adequate” ratings, demonstrating that the items were directly aligned with the
objectives and variables proposed in the research. Likewise, all specialists approved the questionnaire for its final
application, indicating that the instrument presented thematic consistency, appropriate wording, and sufficient
capacity to collect relevant information on neuroeducation, artificial intelligence, and university educational
biometrics.
Regarding the methodological procedure, it began with institutional authorization and informed consent
from the participating students, thereby ensuring the ethical principles of confidentiality, anonymity, and responsible
use of the collected information. Subsequently, the questionnaire was administered virtually through digital forms
distributed to the selected students. Data collection took place during a regular academic term, allowing participants
to respond freely and voluntarily. Once the information was obtained, the data were organized and tabulated in
spreadsheets to facilitate statistical processing and interpretation of the results.
Finally, data analysis was carried out through descriptive statistics, using frequencies, percentages, and
comparative tables that enabled the interpretation of the behavior of the investigated variables. This procedure
facilitated the identification of trends related to student perceptions regarding the use of artificial intelligence and
biometrics within university neuroeducation processes. Likewise, the results were interpreted in light of the
established objectives and the theoretical framework developed throughout the study, allowing coherent
conclusions to be drawn in relation to the problem under investigation in the Bolivian university context.
Results
The following section presents the results obtained from the application of the instrument administered to
Bolivian university students, with the purpose of analyzing students' perceptions regarding the use of artificial
intelligence and biometric analysis in university neuroeducation processes within the context of higher education.
The data collected allowed for the identification of experiences and evaluations associated with the use of intelligent
technologies in academic environments. Likewise, the findings revealed significant trends regarding the acceptance
of AI-based tools, their impact on learning processes, and the relevance of biometric analysis for strengthening
personalized educational processes in higher education institutions.
Results of the survey administered to students
The survey conducted with 120 university students provided relevant information regarding their level of
knowledge, acceptance, and perception of the use of artificial intelligence and educational biometrics in teaching-
learning processes. The results showed a positive attitude toward the implementation of intelligent technologies in
university contexts, particularly in aspects related to academic personalization, improved student performance, and
the strengthening of digital educational experiences. The following statistical tables present the responses obtained
through the Likert-scale instrument.
Table 1
Student perceptions on the use of artificial intelligence in university learning
Options
Frequency
Percentage
Cumulative Percentage
Strongly disagree
6
5%
5%
Disagree
10
8%
13%
Neutral
18
15%
28%
Agree
46
38%
66%
Strongly agree
40
34%
100%
Total
120
100%
Note: Positive perceptions regarding the use of artificial intelligence in university learning
processes predominated / Source: Prepared by the authors
The table shows that the options “Agree” (38%) and “Strongly agree” (34%) were the predominant
responses among the surveyed students. These results indicate that the majority of participants believe that artificial
intelligence has a positive impact on university learning processes, promoting more dynamic and personalized
6
academic experiences. Furthermore, there is a clear positive perception of the use of smart technologies in
educational settings, reflecting a growing acceptance among students of innovative digital tools designed to
enhance academic performance and optimize university teaching processes.
Table 2
Level of acceptance of biometric analysis in educational processes
Options
Frequency
Percentage
Cumulative Percentage
Strongly disagree
8
7%
7%
Disagree
14
12%
19%
Neutral
20
17%
36%
Agree
44
37%
73%
Strongly agree
34
27%
100%
Total
120
100%
Note. Most students accepted biometric analysis as a supportive tool in university
educational processes / Source. Prepared by the authors
The results indicated that the categories “Agree” (37%) and “Strongly agree” (27%) accounted for the
highest number of responses among the participating students. This demonstrates a positive perception of the use
of biometric analysis in university contexts, particularly as a tool for understanding academic and emotional
behaviors. Furthermore, the students considered that biometrics could contribute to improving personalized
educational processes and strengthening academic monitoring. However, a moderate percentage of neutral and
negative responses was also identified, possibly associated with ethical concerns related to privacy and the
management of personal data.
Table 3
Importance of artificial intelligence for improving academic performance
Options
Frequency
Percentage
Cumulative Percentage
Strongly disagree
5
4%
4%
Disagree
11
9%
13%
Neutral
16
13%
26%
Agree
48
40%
66%
Strongly agree
40
34%
100%
Total
120
100%
Note. Students recognized that artificial intelligence contributes to strengthening university
academic performance / Source. Prepared by the authors
The information presented shows that the responses “Agree” (40%) and “Strongly agree” (34%) were the
most prevalent among the university students. These results indicate that the participants recognized the
importance of artificial intelligence as a tool capable of strengthening academic performance and optimizing
learning experiences. Likewise, the evidence showed that students perceived benefits related to educational
personalization, access to digital resources, and improvements in academic organization. Consequently, artificial
intelligence was valued as a strategic technology in contemporary university educational processes.
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Table 4
Perception regarding the ethical use of student biometric data
Options
Frequency
Percentage
Cumulative Percentage
Strongly disagree
12
10%
10%
Disagree
18
15%
25%
Neutral
24
20%
45%
Agree
38
32%
77%
Strongly agree
28
23%
100%
Total
120
100%
Note. Moderate acceptance was identified regarding the ethical use of biometric data
within university environments / Source. Prepared by the authors
The analysis of the findings showed that the options “Agree” (32%) and “Strongly agree” (23%) were the
most frequently selected responses among the university students. This indicates a relatively favorable perception
of the ethical use of biometric data in educational processes, provided that confidentiality and the protection of
personal information are guaranteed. Likewise, the results revealed that a significant proportion of students held
neutral or disagreeing views, reflecting concerns related to privacy, digital surveillance, and the potential risks
associated with the inappropriate management of biometric information within university institutions.
Table 5
Student perceptions of the relationship between smart technologies and motivation
Options
Frequency
Percentage
Cumulative Percentage
Strongly disagree
7
6%
6%
Disagree
13
11%
17%
Neutral
19
16%
33%
Agree
45
38%
71%
Strongly agree
36
29%
100%
Total
120
100%
100%
Note. Intelligent technologies were perceived as tools that increase student
academic motivation / Source. Prepared by the authors
The results revealed that “Agree” (38%) and “Strongly Agree” (29%) were the predominant responses
among the university students surveyed. This data suggests that smart technologies positively influence academic
motivation by fostering greater student participation and engagement in the learning process. Furthermore,
participants felt that AI-based tools create more dynamic, interactive, and adaptive learning experiences.
Consequently, there is a clear positive perception of incorporating innovative technologies into the current university
context.
Discussion
The results obtained demonstrate that the use of artificial intelligence has a positive impact on university
learning processes, especially in aspects related to educational personalization, access to digital resources and
the strengthening of academic performance. In general, the majority of students expressed favorable perceptions
regarding the incorporation of intelligent technologies in university environments, which coincides with what was
pointed out by Cabero-Almenara et al. (2025), who maintain that artificial intelligence optimizes educational
experiences through adaptive environments. Likewise, Pazmiño (2024) states that personalized learning
constitutes one of the main advantages derived from the integration of technology in contemporary higher
education.
8
Similarly, the findings revealed that students recognized the importance of biometric analysis as a
supportive tool for understanding academic and emotional behaviors during university educational processes.
These results are related to the investigations conducted by D’Mello and Graesser (2014), who explained that
affective technologies enable the identification of emotional states associated with attention and learning.
Furthermore, Agualsaca et al. (2025) maintained that affective neuroscience applied to digital educational
environments promotes students’ socioemotional development, strengthening more humanized and cognitively
meaningful academic experiences within contemporary university institutions.
On the other hand, the results reflected that intelligent technologies significantly contributed to increasing
students’ academic motivation, promoting greater participation in university activities mediated through digital
platforms. This situation is consistent with the findings of Bond et al. (2020), who indicated that educational
technologies increase student engagement through interactive and dynamic learning experiences. Likewise,
Zaragoza (2026) affirmed that artificial intelligence promotes academic personalization processes that strengthen
motivation, autonomy, and student retention within university contexts characterized by accelerated digital
transformation and new pedagogical demands.
Nevertheless, the research also revealed concerns associated with the ethical use of biometric data and
the protection of personal information within digitalized university environments. Although most students
demonstrated acceptance of the use of educational biometrics, a significant proportion expressed neutral or
distrustful positions regarding privacy and technological surveillance. These findings are related to the perspectives
proposed by Prinsloo and Slade (2017), who warned about the ethical risks derived from the massive collection of
educational data. Likewise, Durand et al. (2026) maintained that cognitive privacy currently represents a priority
challenge within educational systems based on artificial intelligence.
Furthermore, the obtained findings made it possible to identify that artificial intelligence has the potential to
significantly improve academic performance through adaptive tools aimed at personalized monitoring of university
learning. Students considered that these technologies facilitate immediate access to information, automated
feedback, and more efficient educational experiences. These results coincide with Chen et al. (2022), who
explained that educational artificial intelligence optimizes academic processes through automated analyses and
intelligent recommendation systems. Similarly, Estrella (2025) argued that generative artificial intelligence
strengthens personalized formative assessment processes, promoting more dynamic and contextualized learning
experiences in higher education.
Regarding teaching competencies, the study indirectly evidenced the need to strengthen techno-
pedagogical capacities in order to adequately integrate artificial intelligence and educational biometrics into
university practice. Although students perceived significant benefits associated with these technologies, they also
recognized that their effectiveness depends on the academic and methodological support provided by instructors.
These findings coincide with Noriega et al. (2026), who maintained that digital pedagogical competencies represent
a key factor in the success of educational artificial intelligence. Likewise, Ramírez-Montoya et al. (2023) indicated
that technological innovation in universities requires continuous specialized teacher training.
Another relevant aspect identified in the investigation corresponds to the need to maintain a balance
between technological innovation and educational humanization within contemporary university processes.
Although intelligent technologies facilitate more efficient and personalized academic experiences, students also
valued human interaction as an essential component of meaningful learning. This situation is consistent with the
arguments proposed by Cuéllar et al. (2025), who affirmed that neuroeducation must integrate technological and
emotional dimensions in order to strengthen comprehensive cognitive processes. Furthermore, Downes (2019)
maintained that contemporary learning depends both on digital connections and collaborative human interactions
within dynamic educational networks.
Likewise, the results made it possible to understand that student acceptance of artificial intelligence and
educational biometrics responds to the growing digital transformation occurring within Latin American universities.
New university generations demonstrated greater openness toward technological tools capable of optimizing
academic experiences and strengthening autonomous learning processes. This reality coincides with Bastidas
(2024), who affirmed that higher education faces a prospective scenario characterized by the accelerated
integration of artificial intelligence into educational environments. Similarly, Núñez et al. (2026) maintained that
intelligent technologies promote the development of autonomous learning, although they also generate important
ethical and pedagogical challenges.
Finally, the obtained findings made it possible to conclude that university neuroeducation enhanced by
artificial intelligence and biometric analysis represents an innovative alternative for strengthening teaching-learning
processes within Bolivian higher education institutions. However, its implementation requires ethical policies,
teacher training, and institutional strategies aimed at ensuring a balance between technological innovation and
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student well-being. These results are related to the perspectives proposed by Crompton and Burke (2023), who
maintained that educational artificial intelligence should be implemented through comprehensive and humanized
approaches. Likewise, Freire et al. (2026) warned that the excessive use of intelligent technologies could generate
cognitive dependency in the absence of appropriate pedagogical support.
Conclusions
The research demonstrated that artificial intelligence has a positive impact on university neuroeducation
processes, promoting more personalized, dynamic, and adaptive learning experiences within the context of Bolivian
higher education. Students acknowledged that smart technologies strengthen access to academic resources and
contribute to optimizing educational performance.
The results showed that student biometric analysis is a relevant tool for understanding emotional, cognitive,
and behavioral factors associated with university learning. Most participants considered that these technologies
could significantly contribute to academic monitoring and the early identification of student difficulties.
It was identified that intelligent technologies increase students’ academic motivation and participation,
promoting greater interaction within digital educational environments. Students perceived that artificial intelligence
facilitates more innovative, interactive, and autonomous university learning experiences.
The investigation determined that concerns related to privacy, ethics, and the responsible management of
biometric data exist within university institutions. Although favorable perceptions toward these technologies
predominated, the need to establish clear regulations to ensure the protection and confidentiality of student
information was also evident.
The findings reflected the importance of strengthening teachers’ digital and techno-pedagogical
competencies in order to adequately integrate artificial intelligence tools into university educational processes. The
effectiveness of these technologies depends significantly on the academic and methodological preparation of the
instructors responsible for their implementation.
Finally, it was concluded that university neuroeducation enhanced by artificial intelligence and biometrics
represents an innovative alternative for improving educational quality in Bolivia. Nevertheless, its implementation
requires a balance among technological innovation, pedagogical humanization, and student well-being in order to
guarantee ethical, inclusive, and sustainable educational processes.
References
Agualsaca Calle, D. A., Martínez Hidalgo, F. A., Veintimilla Rizzo, L. M., & Moreno Villalva, E. P. (2025).
Neurociencia afectiva y tecnología educativa: el impacto de las emociones en el aprendizaje y el desarrollo
socioemocional. Sapiens in Education, 2(4), 1–16. https://doi.org/10.71068/1zb2pj75
Area-Moreira, M., & Adell-Segura, J. (2021). Tecnologías digitales y cambio educativo. Una aproximación crítica.
REICE: Revista Iberoamericana de Educación a Distancia, 24(2), 9–21.
https://dialnet.unirioja.es/servlet/articulo?codigo=8086687
Baker, T., Smith, L., & Anissa, N. (2021). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in
schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/
Bastidas González, L. D. (2024). Inteligencia artificial y educación superior: un análisis prospectivo de los desafíos
y oportunidades. Sapiens in Higher Education, 1(1), 1-17.
https://sapiensjournal.ec/index.php/sihe/article/view/42
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student
engagement and educational technology in higher education: a systematic evidence map. International
Journal of Educational Technology in Higher Education, 17, 2.
https://link.springer.com/article/10.1186/s41239-019-0176-8#citeas
Cabero-Almenara, J., Palacios, A., Llorente-Cejudo, C., & Barroso-Osuna, J. (2025). Aceptación de ChatGPT en
educación superior: Actitudes y percepciones del modelo UTAUT2. REICE. Revista Iberoamericana sobre
Calidad, Eficacia y Cambio en Educación, 24(1). https://doi.org/10.15366/reice2026.24.1.001
Chen, L., Chen, P., & Lin, Z. (2022). Artificial intelligence in education: A review. IEEE Access, 10, 75264–75278.
https://ieeexplore.ieee.org/document/9069875
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International
Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-
8
Cuéllar Gaitan, R. A., Betancourt Perdomo, J. D., & Rosales Vidal, E. (2025). Neuroeducación e inteligencia artificial
en contexto educativo: estrategias emergentes para la mejora del rendimiento cognitivo. Imperium
Acadmico. Revista Científica, 2(4), 1-13.
10
https://www.researchgate.net/publication/394573636_Neuroeducacion_e_inteligencia_artificial_en_conte
xto_educativo_estrategias_emergentes_para_la_mejora_del_rendimiento_cognitivoNeuroeducation_and
_Artificial_Intelligence_in_Educational_Contexts_Emergin
D’Mello, S., & Graesser, A. C. (2014). Feeling, thinking, and computing with affect-aware learning technologies. In
R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing.
Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199942237.013.032
Downes, S. (2019). Recent work in connectivism. European Journal of Open, Distance and E-Learning, 22(2), 113–
132. https://eurodljournal.com/articles/10.2478/eurodl-2019-0014
Durand Goyzueta, E. A., Condori Mamani, B., Ramos Chura, E., Avila Inquilla, V. E., Salinas del Carpio, A., Ito
Díaz, R. R., & Hilasaca Zea, C. Y. (2026). Cognitive privacy and the use of artificial intelligence in digital
platforms for higher education. NeuroData, 3, 160. https://doi.org/10.63688/neurodata2026160
Estrella Romero, V. A. (2025). Impacto de la inteligencia artificial generativa en la evaluación formativa universitaria
personalizada. Sapiens in Higher Education, 2(10), 1–14. https://doi.org/10.71068/fw0q7v55
Freire Palacios, V. A., Torres Vizuete, R. E., Alcivar Cuenca, P. E., Cedeño Casquete, G. C., & Ordóñez Ruiz, M.
M. (2026). Uso de inteligencia artificial y dependencia cognitiva en estudiantes universitarios. Sage Sphere
of Technology, Sciences, Discoveries and Society, 4(1), 1-23. https://doi.org/10.63688/znqmqg17
Giannakos, M., Sharma, K., Pappas, I., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to
understand the learning experience. International Journal of Information Management, 48, 108-119.
https://www.sciencedirect.com/science/article/pii/S0268401218312751?via%3Dihub
Hernández-Sampieri, R. & Mendoza, C (2018). Metodología de la investigación. Las rutas cuantitativa, cualitativa
y mixta. Mc Graw Hill Education.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for
Teaching and Learning. Center for Curriculum Redesign. https://www.consorciosthem.com/wp-
content/uploads/2025/02/sthem-ia-07-holmes-fadel-bialik-artificial-intelligence-in-education-promise-and-
implications-for-teaching-and-learning-2019.pdf
Immordino-Yang, M., & Damasio, A. (2021). We feel, therefore we learn: The relevance of affective and social
neuroscience to education. Mind, Brain, and Education, 1, 3-10. https://doi.org/10.1111/j.1751-
228X.2007.00004.x
Knox, J., Wang, Y., & Gallagher, M. (2019). Artificial intelligence and inclusive education speculative futures and
emerging practices: Speculative futures and emerging practices. Springer Singapore.
https://link.springer.com/book/10.1007/978-981-13-8161-4#accessibility-information
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence unleashed: An argument for AI in education.
Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-
pearson/innovation/open-ideas/IntelligenceUnleashedSPANISH.pdf
Noriega Medina, M. B., Romero Berrones, W. J., Manssur Ponce, I. M., Hidalgo Solórzano, J. L., & Contento
Castillo, R. A. (2026). Uso de herramientas de inteligencia artificial y competencias pedagógicas en
docentes de educación superior. Sage Sphere Multidisciplinary Studies, 3(1), 1-23.
https://doi.org/10.63688/6b6qwk94
Núñez Cayetano, A. M., Lopez Torres, M. F., Strasser López, K. L., & Herrera Vera, D. E. (2026). Inteligencia
artificial en la educación: beneficios, desafíos éticos y su impacto en el aprendizaje autónomo. Sapiens in
Artificial Intelligence, 3(2), 1-16. https://doi.org/10.71068/jgk10c67
Pazmiño Arcos, A. F. (2024). Revolucionando la educación superior: inteligencia artificial para el aprendizaje
personalizado y la transformación docente. Sage Sphere of Technology, Sciences, Discoveries and
Society, 2(2), 1-13. https://doi.org/10.63688/ftdznh26
Prinsloo, P., & Slade, S. (2017). Ethics and learning analytics: Charting the (un)charted. In C. Lang, G. Siemens,
A. F. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 49–57). Society for Learning Analytics
Research. https://doi.org/10.18608/hla17.004
Ramírez-Montoya, M.S., González, C. S. G., Montoya, D. H., Lopez-Caudana, E. O., & Rodríguez-Abitia, G. (Eds.)
(2023). Open education for sustainable development: Contributions from emerging technologies and
educational innovation. Frontiers Media S. A. https://hdl.handle.net/11285/651091
Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Publications
Office of the European Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC107466
Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education
for minority students: A systematic review. Sustainability, 14(20), 13572. https://www.mdpi.com/2071-
1050/14/20/13572
11
Zaragoza Alvarado, G. A. (2026). Influencia de la Inteligencia Artificial en los procesos de personalización del
aprendizaje en la educación superior. Sapiens in Artificial Intelligence, 3(1), 1-15.
https://doi.org/10.71068/asq6b765
AUTHOR CONTRIBUTIONS:
Wilson Angel Gutierrez Rodriguez: Conceptualization; Formal analysis; Funding acquisition; Investigation;
Methodology; Project administration; Resources; Supervision; Visualization; Writing – original draft.
Marianela León Mendoza: Conceptualization; Data curation; Investigation; Resources; Supervision; Validation;
Writing – review and editing.
Graciela Mamani Torres: Data curation; Investigation; Validation; Writing – original draft.
Pamela Navarro Escobar: Formal analysis; Investigation; Methodology; Software; Visualization; Writing – review
and editing.
