Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Artificial Intelligence and the Teaching Process
for Economics Students
La inteligencia artificial y el proceso de enseñanza en
estudiantes de Ciencias Económicas
Santiago Vinueza-Vinueza
Universidad César Vallejo, Piura, Perú
Doctorado en Educación
svinuezav@ucvvirtual.edu.pe
https://orcid.org/0009-0008-7159-6098
Alejandra Fonseca-Factos
Ministerio de educación, Quito, Ecuador
Unidad Educativa Uyumbicho
sonia.fonseca@educacion.gob.ec
https://orcid.org/0000-0002-2103-9698
(Received on: 11/04/2025; Accepted on: 1/06/2025; Final version received on: 05/01/2026)
Suggested citation: Vinueza-Vinueza, S. y Fonseca-Factos, A. (2026). Artificial Intelligence
and the Teaching Process for Economics Students. Revista Cátedra, 9(1), 35-52.
Abstract
Artificial intelligence drives knowledge, facilitates the comparison of diverse perspectives,
and provides information tailored to student needs. In the teaching process, it promotes
various strategies, tools, and methodologies that are revolutionizing the educational field.
In this sense, within university settings, the teacher acts as a guide and facilitator, shaping
a pedagogical and cognitive process. This approach is grounded in an ethical awareness that
avoids excessive dependence, inequality, or misinformation. This study employed a
quantitative approach with a non-experimental design. The research was cross-sectional,
collecting data at a single point in time, and had an explanatory scope aimed at addressing
the causes of social phenomena related to higher education and Artificial Intelligence (AI).
The population consisted of 243 university students enrolled in the second and third
semesters of the Statistics, Economics, and Finance programs at the Central University of
Ecuador. To obtain a sample size of 149 students, simple random sampling (SRS) and
proportional stratified sampling were used to ensure equitable representation according to
36
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
academic level (68 second-semester students and 81 third-semester students). The results
show that, although students demonstrate a moderate level of ethical awareness, gaps
remain in their comprehensive understanding of some theoretical constructs related to the
ethical use of AI. This situation highlights the need to incorporate specific content into
educational programs aimed at strengthening ethical considerations in the use of these
technologies.
Keywords
Artificial intelligence, educational innovation, economic sciences, ethical awareness,
academic performance.
Resumen
La inteligencia artificial impulsa el conocimiento, brinda facilidad para cotejar diversas
perspectivas y brindar información conforme a las necesidades del estudiante. En el proceso
de enseñanza promueve varias estrategias, herramientas o metodologías que revolucionan
el campo educativo. En este sentido, en los entornos universitarios el docente es un guía y
moderador, a fin de construir un proceso pedagógico y cognitivo. De este modo, se parte de
una conciencia ética que no incurre en dependencia excesiva, desigualdad o desinformación.
Este estudio se basó en un enfoque cuantitativo, con diseño no experimental. La
investigación tuvo un corte transversal, recolectó datos en un solo momento, y un alcance
explicativo para responder a las causas de los fenómenos sociales sobre la educación
superior y la Inteligencia Artificial (IA). La población se conformó por 243 estudiantes
universitarios matriculados en el segundo y tercer semestre de las carrereas de Estadística,
Economía y Finanzas de la Universidad Central del Ecuador. Para obtener un tamaño de
muestra de 149 estudiantes se utilizó un muestreo aleatorio simple (MAS) y un muestreo
estratificado proporcional para garantizar una representación equitativa según el nivel
académico (68 estudiantes de segundo semestre y 81 de tercer semestre). Los resultados
evidencian que, aunque los estudiantes presentan un nivel moderado de conciencia ética,
persisten vacíos en la comprensión integral de algunos constructos teóricos relacionados
con el uso ético de la IA. Esta situación pone de manifiesto la necesidad de incorporar,
dentro de los programas formativos, contenidos específicos orientados a fortalecer la ética
en la utilización de estas tecnologías.
Palabras clave
Inteligencia artificial, innovación educativa, ciencias económicas, conciencia ética,
rendimiento académico.
1. Introduction
The rapid technological evolution of recent decades has significantly transformed
educational systems, generating new teaching and learning scenarios that demand the
incorporation of innovative digital tools. In this context, according to Numa-Sanjuán et al.
and Vera, AI has established itself as one of the emerging technologies with the greatest
impact on 21st-century education, due to its capacity to process large volumes of
information, personalize learning, and optimize pedagogical processes (Numa-Sanjuán et
al., 2024; Vera, 2023). Its integration into higher education not only responds to a
technological trend but also to the need to strengthen educational quality and prepare
students to face the challenges of an increasingly digitalized academic and professional
environment.
37
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Several studies agree that AI offers significant opportunities to innovate traditional teaching
methodologies by enabling the design of more dynamic, interactive, and student-centered
learning environments (Baltazar, 2023; Peñaherrera et al., 2022). These tools facilitate the
adaptation of content to learning paces and styles, promoting more meaningful and effective
learning processes. In particular, Jardón et al. and Ludeña-Yaguana consider that higher
education has shown significant progress in the use of virtual assistants, recommendation
systems, and intelligent platforms that contribute to improving academic performance and
student motivation (Jardón et al., 2024; Ludeña-Yaguana et al., 2025).
In the field of Economics, the incorporation of artificial intelligence is especially relevant,
given that these disciplines require the constant analysis of data, informed decision-making,
and the development of critical thinking. Recent research by Espinales-Franco, and Ribas
and Provasi indicates that the strategic use of AI in this field fosters the understanding of
complex content, the development of analytical skills, and the improvement of students'
academic performance (Espinales-Franco et al., 2024; Ribas and Provasi, 2024). Similarly,
Biggs states that AI enables the implementation of more formative assessments and the
continuous monitoring of learning, aligning with student-centered pedagogical approaches
(1987).
However, the integration of artificial intelligence into educational processes also presents
significant challenges, especially regarding teacher training, ethics, and the responsible use
of technology. Vera and Piedra-Castro et al. indicate that the teacher's role is transforming,
shifting from a transmitter of knowledge to a pedagogical mediator capable of guiding
learning through the critical and reflective use of AI (Vera, 2023; Piedra-Castro et al., 2024).
In this sense, it is essential that educators develop digital competencies that allow them to
leverage the potential of these tools without affecting the development of critical thinking
or students' intellectual autonomy (Vélez-Rivera et al., 2024).
From an ethical perspective, various authors, such as Ruiz-Miranda and Vélez-Rivera, warn
that the indiscriminate use of artificial intelligence can generate technological dependence,
biases in information, and limitations in critical analysis if clear guidelines are not
established for its application in the university setting (Ruiz-Miranda, 2023; Vélez-Rivera et
al., 2024). Therefore, AI should be conceived as a means of supporting the educational
process and not as a substitute for pedagogical judgment, always maintaining the teacher's
central role in regulating knowledge and in the student's holistic development.
Within this framework, this research aims to analyze the relationship between artificial
intelligence and the teaching-learning process in Economics students, considering its
technological, pedagogical, cognitive, evaluative, and ethical implications. From a
comprehensive perspective, the study seeks to provide empirical evidence that allows us to
understand the real impact of AI in higher education and contribute to the development of
innovative teaching strategies that strengthen the quality of the educational process.
Regarding the article's structure, Section 2 presents a review of the scientific literature on
artificial intelligence and its impact on the teaching and learning process for Economics
students. Section 3 describes the methodology used for the research, specifying the
approach, design, population, sample, and instruments employed. Section 4 presents and
analyzes the results obtained concerning the impact of artificial intelligence on the
technological, pedagogical, cognitive, evaluative, and ethical dimensions of the educational
process. Subsequently, Section 5 discusses the results, contrasting them with previous
38
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
studies and relevant theoretical contributions. Finally, Section 6 presents the conclusions,
which are derived from the study's findings and allow for a comprehensive assessment of
the contribution of artificial intelligence to the teaching of Economics.
2. Literature Review
2.1. AI in university environments and its impact on the teaching process
Artificial Intelligence (AI) is a revolutionary mechanism that marks a turning point in social
spheres by providing specific assistance. In education, pedagogical paradigms are being
reconstructed to achieve meaningful learning. Thus, “AI is defined as a branch of computer
science that focuses on the development of intelligent agents capable of reasoning, learning,
and making decisions autonomously” (Jardón et al., 2024, p. 5). Therefore, AI constitutes a
process that focuses on large amounts of information, enabling problem-solving, teaching
and learning, and decision-making. Its practical application lies in the didactics of teaching
as a mechanism that transforms methodologies and proposes efficient tools that strengthen
competencies.
At the same time, AI in university settings proposes a construction of thought that
strengthens students' knowledge. It allows for machine learning, data analysis, and rapid
information processing that provides feedback to the learning process. In this sense, “AI’s
ability to personalize learning, adapting educational content to students’ individual needs,
promises to significantly increase academic performance and student motivation” (Piedra-
Castro et al., 2024, p. 123). Therefore, the implementation of this technology in university
settings enhances knowledge and streamlines data processing through didactic and
innovative explanations. However, it depends on the conditions or infrastructure of these
spaces to establish a pedagogical focus that achieves teaching objectives.
Similarly, the substantial value of AI in university education lies in its ability to identify
knowledge gaps, with the aim of improving academic performance. Furthermore, “AI can
adapt the teaching-learning process to the individual needs and preferences of each student,
offering learning resources and activities tailored to their level of knowledge, learning style,
and pace of progress” (Vera, 2023, p. 20). Therefore, students benefit from an innovative
methodology that meets their needs and learning styles. AI proposes student-centered
activities, promoting autonomous learning. This contributes to teaching and reduces the
teaching load, allowing teachers to redirect their time to lesson planning.
AI not only positively impacts student learning but also benefits teachers in administrative
tasks. Educators consider this tool to be a valuable resource that contributes to didactic and
pedagogical effectiveness. For this reason, "teachers highlight that AI reduces the workload
in activities such as grading exams and managing attendance, allowing them to focus more
on teaching" (Ludeña-Yaguana et al., 2025, p. 232). Consequently, teachers take a strategic
position, becoming learning designers with technology that guarantees knowledge
acquisition. This educational co-creation tool contributes to the design of teaching activities,
aligns pedagogical objectives, and enables teaching with an educational purpose.
Thus, the impact of AI on the teaching process stems from an innovative transformation that
provides dynamic, flexible, and personalized spaces for students. Meanwhile, “the primary
advantages of implementing AI in teaching are the construction of algorithms that grade
these forms, freeing up teachers more time to research, develop innovative teaching
methodologies, and provide individualized attention to their students” (Peñaherrera et al.,
2022, p. 407). The tool adapts content, activities, and assessments to suit the teacher's
approach and ensure each student achieves the appropriate level of understanding. This
39
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
allows teachers to generate teaching materials, analyze each student's progress, consolidate
individualized learning, and develop logical reasoning and motivation.
2.2. AI tools used for the teaching process of Economic Sciences: pedagogical
and cognitive process
The revolutionary shift in the use of Information and Communication Technologies (ICTs)
within university settings precedes a restructuring of the pedagogical process, as it offers
solutions to educational challenges in the classroom. Artificial intelligence seeks to enhance
the human capabilities of teachers, in order to provide instruction that fosters critical
thinking and guarantees knowledge acquisition. Thus, AI “is a science that helps to
accurately diagnose each student individually in order to design a series of activities that
will lead to the improvement of their main weaknesses” (Numa-Sanjuan et al., 2024, p. 51).
For this reason, the student's educational development fosters a conducive environment in
which skills, knowledge, and values are integrated, aiding in the practice of Economic
Sciences.
AI allows for the exploration of a range of information associated with innovative
methodologies or strategies that facilitate the understanding of content. Similarly, the tools
used in the teaching process are replacing traditional paradigms by implementing
interactive environments that promote meaningful learning. The integration of AI in
Economics facilitates the understanding of topics such as data processing, finance, statistics,
and calculations, among others. Therefore, intelligent tutors have the ability to collect data,
investigate trends, and assess economic aspects to strengthen students' knowledge.
In this sense, personalized teaching emerges in the pedagogical field, as it is a mechanism
that identifies needs and reconstructs them to provide learning solutions. In particular,
"chatbots are capable of providing a personalized and flexible learning experience, which
could lead to an increase in academic performance and student satisfaction" (Ruiz-Miranda,
2023, p. 158). Consequently, this tool acts as a virtual assistant that offers coherent answers,
with the aim of guiding specific learning processes. Teaching is student-centered, making
the student the central figure in knowledge acquisition, allowing them to authentically
appropriate it in their professional development. Similarly, the most used tool by students
is ChatGPT, which has become a means to search for information or organize statistical data.
In the pedagogical process, it allows for independent work, analysis of statistical tables,
simulations, or practical cases in finance, administration, or business. For this reason, “the
application of ChatGPT is not limited to independent learning, but can also be used in the
classroom to encourage student participation and improve peer interaction, as well as
interaction with the teacher” (Ruiz-Miranda, 2023, p. 158). The intended use of this tool in
education allows for strategic, interactive, and teacher-assistive teaching, identifying
difficult-to-understand content and making it didactic for knowledge retention.
The use of AI in the cognitive process enables independent learning to organize thinking,
promotes reasoning, and designs learning situations. It involves processing information
through content evaluation, concentration, and knowledge comprehension. In turn,
“artificial intelligence systems have the potential to increase students’ cognitive abilities,
stimulating the generation of alternative perspectives” (Palma-Landirez et al., 2024, p.
4023). In this sense, the cognitive paradigm combined with AI encompasses the student’s
information processing. Thus, their learning process is recognized and integrated with
strategies that should be used to acquire knowledge meaningfully.
40
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Furthermore, within the cognitive process, Intelligent Tutoring Systems (ITS) stand out as
an advantageous tool. Thus, “an intelligent tutor plays the role of a personalized tutor for
each student, meaning it can identify their individual needs and the metacognitive
processes required for their learning” (Baltazar, 2023, p. 8). Therefore, intelligent tutors
offer continuous and evolving monitoring according to the learner's needs, guided problem-
solving, and immediate feedback. They also strengthen attention, memory, and the
development of logical-mathematical reasoning, among other skills that consolidate
analytical competencies.
Next, Learning Management Systems (LMS) provide development of cognitive and
metacognitive skills through interactive courses personalized to the student's needs. These
platforms analyze learning styles and structure learning paths, resources, assessments, and
tasks that encourage attention, memory, and comprehension. Thus, “AI-powered LMSs offer
tools for tracking academic progress, allowing both students and teachers to monitor
progress in real time and adjust the educational process as needed” (Rosero & Guevara,
2025, p. 6137). Therefore, these systems foster the development of critical thinking, active
participation, performance monitoring, and stimulate cognitive processes (reflection,
analysis, reasoning). In line with Economics, LMSs strengthen learner autonomy. For this
reason, students self-regulate their learning through activities such as forums, assessments,
and tasks related to accounting, economics, and administration, in order to develop their
thinking through data analysis. The enhancement of logical thinking, quantitative analysis,
and decision-making through these platforms catalyzes teaching and learning. In short, the
system meets educational needs: on the one hand, teachers establish activities as
knowledge guides; on the other hand, students seek alternatives to promote logical and
methodical reasoning.
2.3. Ethical awareness in the use of AI
The usefulness of technology entails a responsibility of awareness and critical thinking to
use it as a means to enhance skills. Ethical awareness in the use of AI takes into account
risks such as dependency, data projection, biases, equity, and transparency in knowledge
construction. Indeed, “ethics in educational AI is fundamental to protecting privacy,
avoiding biases, and ensuring equity in access to and use of these technologies” (Lima et al.,
2025, p. 6). Consequently, the use of AI in educational settings analyzes the benefits and
risks associated with the indiscriminate use of this tool with respect to the development of
analytical thinking. Likewise, ethical application integrates moral reflection when making
decisions based on responsible, honest, and authentic values that support knowledge.
The combination of AI with higher education presents specific challenges stemming from
the infrastructure and its ethical use. When integrating this tool into academic
environments, it is essential to train educators and students to avoid disadvantages such as
excessive dependency. Indeed, “it is necessary to establish clear policies and ethical
regulations to guide the development, implementation, and use of AI in education, with the
aim of ensuring that equity, inclusion, and the well-being of all students are promoted”
(Espinales-Franco et al., 2024, p. 4734). For this reason, comprehensive and holistic training
enables the proper functioning of AI in education, in order to implement it as a means of
strengthening basic and transversal skills.
In this sense, AI in university settings depends on faculty regulation and the autonomy
students have to understand its proper functioning. Ethical awareness responds to the need
to establish values or principles for decision-making. Some of the ethical problems present
are “the expansion of marginalization, inequality, inequity, injustice, and discrimination
41
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
existing in society, through the analysis and generation of biased, opaque, or inexplicable
data” (Vélez-Rivera et al., 2024, p. 4). That is, AI should be used as a learning support tool,
avoiding its replacement with knowledge. The teacher, as the primary guide, contributes to
establishing limits that allow for transparency, responsibility, and critical thinking, while
maintaining the pedagogical focus of teaching.
The information provided by AI generates a conglomeration of perspectives that can
increase misinformation in academic cases. The indiscriminate use of this tool limits the
reflective and analytical development of students, leading them to complacency or
conformity in completing tasks. “In many cases, students may use these tools to generate
content that is not original, which raises serious doubts about the value of learning and
authorship in the academic sphere” (Ribas and Provasi, (2024, p. 5). Therefore, there is no
policy development that guarantees the appropriate use of AI; however, the work of
teachers allows them to raise awareness and sensitivity regarding the potential of these
tools in academic use. Ultimately, ongoing training not only allows for the development of
teaching methodologies but also provides a conscious understanding of the conflicts that
arise in the teaching process.
3. Methods and materials
This research was conducted using a quantitative approach, which allowed for the objective
measurement and analysis of the relationship between the use of artificial intelligence and
the learning approaches adopted by higher education students. A non-experimental design
was employed, as the study variables were not deliberately manipulated but rather
observed as they occur in their natural context. This approach is relevant for analyzing
educational phenomena associated with pedagogical practices mediated by emerging
technologies.
The study was cross-sectional, with data collection occurring at a single point in time. This
provided a snapshot of the current state of students' learning processes in relation to the
integration of artificial intelligence in higher education. Furthermore, the research was
explanatory in scope, aiming to identify and analyze the potential causes and relationships
between learning approaches and educational dynamics influenced by the use of AI tools in
the university setting.
The Study Processes Questionnaire (SPC), a widely validated instrument in educational
research, was used for data collection. It consists of 42 items organized around three
learning approaches: surface, deep, and high-performance or achievement. Each of these
approaches assesses both the motives and strategies students employ during their learning
process using a Likert-type scale. The questionnaire is based on Biggs' (1987) theory of
learning approaches, which posits that university learning is explained by the interaction
between student motivations and the cognitive strategies they use to tackle academic tasks.
In the context of this research, the SPC allowed for the analysis of how these approaches
manifest themselves in an educational environment influenced by the use of artificial
intelligence.
The study population consisted of 243 university students enrolled in the second and third
semesters of the Statistics, Economics, and Finance programs at the Central University of
Ecuador. The selection of students from these academic levels was based on two main
criteria: first, this is a key formative stage in the consolidation of learning habits and
approaches; and second, the curricular proximity between semesters reduced academic
variability and ensured greater homogeneity in the sample. Simple random sampling was
42
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
used to determine the sample size, supplemented by proportional stratified sampling, to
ensure equitable representation of students according to their semester. The result was a
sample of 149 students, distributed as 68 second-semester students and 81 third-semester
students, allowing for a balanced comparative analysis between the two groups.
Data collection was carried out using the Google Forms platform, a tool that facilitated the
efficient administration of the questionnaire and guaranteed the anonymity and
confidentiality of the responses. Subsequently, the data obtained were processed and
analyzed using IBM SPSS Statistics version 29.0, employing rigorous criteria for statistical
analysis and hypothesis validation, with a significance level of 5% (α = 0.05).
For the statistical analysis, descriptive techniques such as frequencies, means, and standard
deviations were used to characterize students' learning approaches. Inferential tests were
also applied to identify significant relationships and differences between the study
variables, including Student's t-test, Pearson's correlation coefficient, and non-parametric
tests such as Kendall's and Spearman's tau-b, selected based on the data distribution. These
techniques enabled a deeper understanding of the impact of the AI-mediated educational
context on teaching and learning processes in higher education.
4. Analisis and Results
Through the analysis of the tables and graphs shown below, the behavior of the study unit
will be observed in order to gain a better understanding of the characteristics identified in
the research.
Table 1 shows that the majority of surveyed students are from the Finance program
(59.7%), followed by the Statistics program (24.2%) and the Economics program (16.1%).
This distribution was relevant because the application and perception of AI can vary slightly
among these disciplines within Economics. Furthermore, the sample distribution for this
academic unit is concentrated in the area related to the social sciences (75.8%), while the
applied statistics area represents 24.2% of the sample.
Major
Frequency
Percentage
Cumulative Percentage
Economics
24
16.1 %
16.1 %
Finance
89
59.7 %
75.8 %
Statistics
36
24.2 %
100.0 %
Total
149
100.0 %
Cuadro 1. Distribución de los estudiantes según la carrera de estudio
According to Table 2, there is an even distribution between second- and third-semester
students, with percentages of 45.6% and 54.4%, respectively. This distribution by academic
level was intentional, allowing for control of variability in university experience and
familiarity with the content of Economics. By concentrating the sample in these
intermediate semesters, it was ensured that participants had a similar knowledge base,
which is crucial for more accurately evaluating the influence of AI on their learning process,
preventing differences in their level of academic progress from being a distorting factor.
43
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Semester
Frequency
Percentage
Cumulative Percentage
Second semester
68
45.6 %
45.6 %
Third semester
81
54.4 %
100.0 %
Total
149
100.0 %
Table 2. Distribution of students by semester
Table 3 shows the equitable gender distribution, with 51.0% women and 49.0% men in the
total sample. This distribution ensured that the study results were not biased by gender,
allowing us to infer that the impact of AI on the learning process was evaluated from a
balanced perspective. Furthermore, this distribution ensured the external validity of the
findings, as the sample reflects gender diversity, strengthening the generalizability of the
conclusions.
Semester
Frequency
Men
Women
Percentage
Second semester
68
33
35
51.0 %
Third semester
81
40
41
49.0 %
Total
149
100.0 %
Table 3. Distribution of students by sex
Analysis of Table 4 reveals that the majority of surveyed students are between 18 and 21
years old, representing 73.2%. This concentration at early stages of university life is
consistent with the second- and third-semester student population, resulting in a sample
composed primarily of students who are actively engaged in their studies and have grown
up in an increasingly digital environment.
Age range
Frequency
Percentage
Cumulative Percentage
Between 18 y 19 Y. O
35
23.5%
23.5%
Between 20 y 21 Y.O.
74
49.7%
73.2%
Between 22 y 23 Y. O
26
17.4%
90.6%
Between 24 y 25 Y. O
7
4.7%
95.3%
Older than 25 Y. O
7
4.7%
100.0%
Total
149
100.0%
Table 4. Distribution of students according to age range
44
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
To evaluate how artificial intelligence impacts the assessment dimension in Economics
students, with particular emphasis on ethical awareness regarding the use of these
technologies in the educational field, the following hypotheses are proposed for analysis:
•Alternative hypothesis (Hi): Artificial intelligence positively influences the
evaluation dimension, promoting greater ethical awareness and responsibility in its
use by students.
Null hypothesis (H0): Artificial intelligence has little impact on ethical awareness.
The descriptive analysis of the level of ethical awareness revealed that 41.6% of the
students are in the low categories (1 and 2), while 58.4% reach the medium and high
categories (levels 3 to 5), as shown in Figure 1.
Figure 1. Level of ethical awareness about the use of AI, distribution by levels and sex (n=149)
The results of the independent samples t-test (Table 5) indicated no statistically significant
differences in ethical awareness or in the AI assessment dimension among the different
demographic groups analyzed (all two-tailed p-values were greater than .05, for CPE_P1
p=.207, for Age p=.234). This suggests that, although most students have moderate ethical
awareness, significant gaps remain in their in-depth understanding of ethical issues, and the
impact of AI on assessment is uniform across groups.
Variable
F de
Levene
p de
Levene
T
Gl
p (bilateral)
Difference of
means
Standard
error
CPE_P1
0.863
.355
1.268
147
.207
0.231
0.183
Age
1.285
.259
1.194
147
.234
0.195
0.163
Semester
0.042
.837
0.103
147
.918
0.008
0.082
TICS_P1
1,791
.183
-0.665
147
.507
-0.025
0.037
Performance
2.029
.156
-0.298
147
.766
-0.039
0.130
Motivation
0.780
.379
0.390
147
.697
0.042
0.107
Table 5. Independent samples t-test on the influence of artificial intelligence on ethical awareness and the
evaluation dimension
45
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Note: Levene's test showed no significant differences (p > .05) in any case. CI = confidence
interval; df = degrees of freedom.
The overall findings support the general hypothesis (Hi) that AI has a positive impact on the
teaching-learning process of Economics students, with p-values < 0.001 for the main
variables. For the specific hypotheses:
Technological: It is confirmed that AI has a significant effect on students' use of
technological tools, although the impact is based more on its pedagogical application
than on direct access.
Pedagogical: AI considerably enhances the educational aspect, increasing
motivation and optimizing the perceived teaching process.
Cognitive: AI has an indirect impact on the cognitive dimension, fostering
motivation that affects academic performance.
Assessment: AI promotes more individualized and continuous assessment, although
its impact is equitable and does not vary according to demographic group.
Ethical Awareness: AI promotes a moderate level of ethical awareness, although
significant differences still exist in its critical understanding.
The results show that, although students demonstrate a moderate level of ethical
awareness, gaps remain in their comprehensive understanding of some theoretical
constructs related to the ethical use of AI. This situation highlights the need to incorporate
specific content into educational programs aimed at strengthening ethical awareness in the
use of these technologies.
Furthermore, the study's findings confirm the general hypothesis (Hi), which posits that AI
has a positive impact on the teaching and learning process of Economics students at the
Central University of Ecuador in 2024. This assertion is supported by highly significant p-
values (p < 0.001), demonstrating that the averages obtained for the main variables
significantly exceed the reference value. Consequently, the null hypothesis (H0) is rejected,
reaffirming that the impact of AI is not a random effect, but rather a real and beneficial one.
Regarding the specific hypotheses, solid evidence was obtained that allowed for their
confirmation. In the technological sphere, it was found that AI is applied and understood by
students, supporting the acceptance of the corresponding alternative hypothesis. In the
educational aspect, the results indicate that AI significantly influences student motivation,
promoting active participation in the teaching-learning process. From a cognitive
perspective, it was determined that AI fosters the development of meaningful learning and
stimulates critical thinking, fundamental aspects for strengthening academic skills and
abilities. Regarding the evaluation process, the results show that AI facilitates task
assessment and the immediate generation of error reports, enabling systematic and
continuous feedback on learning.
Finally, it is important to highlight that no significant differences were identified based on
the students' gender, age, or semester. This finding suggests that the influence of AI is
uniform and equitable, regardless of the participants' demographic characteristics or
geographic location.
46
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
5. Discussion
The results obtained in this research show that the level of ethical awareness in the use of
artificial intelligence is in the average range, a significant finding when triangulating
empirical data with existing theoretical contributions. This suggests that, while students
recognize the importance of responsible AI use, gaps in their training still exist, limiting a
solid and critical ethical understanding. This result coincides with the findings of Gómez-
Cárdenas et al., who warn of the urgent need to incorporate digital ethics as a cross-cutting
theme in the university teaching and learning process (2024). Along the same lines,
Espinosa-Vallejo et al. emphasize that educational transformation should not focus
exclusively on technological adoption, but rather on comprehensive and holistic academic
training capable of developing ethical awareness and critical thinking in students (2025).
By correlating these findings with levels of competence in artificial intelligence, the results
show that greater familiarity with and academic use of AI are associated with a greater
ethical awareness and responsibility in its application, reinforcing the arguments of
Morocho-Pintag et al. (2025). These authors maintain that AI should not be conceived solely
as an instrumental resource, but as a tool with the potential to transform learning processes,
provided its integration is accompanied by clearly defined pedagogical and ethical criteria.
From the perspective of Economics, artificial intelligence has proven to be a key tool for
process automation, the analysis of large volumes of data, and predictive decision-making.
However, the results of this study reveal that its educational impact requires the explicit
incorporation of competencies such as programming, predictive analytics, computational
thinking, and algorithmic ethics. This finding aligns with the views of González-Alarcón and
Melguizo (2023), who point out that the training of future professionals must respond to
the new demands of the labor market without neglecting the ethical dimension. Likewise,
this need is consistent with the arguments of Lamas-Lara et al., who maintain that critical
and reflective thinking should be a fundamental pillar of university education (2025).
Triangulating the results with previous studies reveals that the inclusion of artificial
intelligence in the educational process faces structural obstacles, such as institutional
limitations, insufficient teacher training, and inadequate curricular adaptation. These
findings confirm the observations of Rochina et al., who emphasize that the successful
integration of emerging technologies depends not only on technological advancement but
also on teacher training and curricular coherence (2024). Despite this, the study's results
demonstrate that AI has high potential to increase motivation and optimize academic
performance, which aligns with the findings of Sánchez-Salazar et al. (2024).
In this context, Ponce-Tituaña et al. (2025) emphasize the need to strengthen digital
education from a holistic perspective, incorporating not only technical skills but also ethical
foundations and an understanding of its operation. This view is triangulated with the results
obtained in the study and with the proposals of Menacho-Ángeles et al., who highlight the
importance of training critical, responsible, and conscious users of artificial intelligence-
based technologies (2024). On the other hand, although the study did not identify
statistically significant differences in the perception and use of AI according to gender or
academic semester, this result should not be interpreted as evidence of equitable access to
technology. As Rodríguez-Martínez et al. point out, digital divides are due to social, cultural,
and structural factors (2025), which are not always directly reflected in the variables
analyzed. In this regard, Armijos et al. highlight that AI has the potential to personalize
47
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
content according to the student's learning pace and style (2025); however, the results of
this study show that such personalization has not yet been fully achieved due to insufficient
pedagogical integration and limited teacher training.
Finally, when correlating the empirical results with technology adoption models, it is
observed that the perception of usefulness and ease of use continues to be a determining
factor for the acceptance of AI in educational settings. As Pita-Briones et al. maintain, the
effectiveness of artificial intelligence in education depends not only on the technology itself,
but also on the institutional environment, educational policies, and teacher training (2025),
elements that are consistently reflected in the study's findings. In summary, the
triangulation of the results allows us to conclude that artificial intelligence constitutes a
strategic resource for strengthening the teaching-learning process in Economics students,
provided that its integration is carried out in a planned, ethical, and pedagogically sound
manner. AI-based tools not only enhance key technological competencies for professional
training, but also contribute positively to academic performance and the development of a
responsible ethical awareness in the use of emerging technologies.
6. Conclusions
The triangulation of empirical results, statistical analysis, and scientific literature allows us
to conclude that artificial intelligence (AI) has a positive and significant influence on the
teaching and learning process of Economics students. Correlational analyses revealed a
direct relationship between the level of AI competence and variables associated with
academic performance, motivation, and student participation, confirming that AI, when
used for pedagogical purposes, contributes to revitalizing traditional educational practices
and improving the understanding of complex content specific to these disciplines.
From an educational perspective, the results show that the integration of AI fosters
autonomous and personalized learning, allowing for the adaptation of content, activities,
and assessment processes to students' learning paces and styles. This personalization
correlates with the strengthening of key cognitive skills in Economics, such as data analysis,
logical reasoning, and informed decision-making. Triangulation with previous studies
confirms that these benefits are enhanced when AI is integrated in a planned manner and
aligned with curricular objectives.
Regarding the ethical dimension, the statistical results showed a positive and significant
correlation between the level of competence in artificial intelligence and ethical awareness
in its use, allowing us to accept the proposed alternative hypothesis. However, the average
level of ethical awareness achieved indicates that this aspect is not yet fully consolidated in
university education. Comparing these findings with the literature reaffirms the need to
incorporate digital ethics as a cross-cutting theme in the educational process, avoiding
uncritical or technology-dependent practices.
Likewise, the correlation between the use of artificial intelligence and the perception of its
pedagogical utility confirms that teacher mediation is a determining factor for its
effectiveness. Triangulation of the results shows that AI does not replace the teacher's role,
but rather redefines it, assigning them a strategic function as a guide and regulator of
learning. In this sense, teacher training and instructional planning emerge as essential
conditions for AI to contribute to the development of critical thinking and the active
construction of knowledge. Finally, based on the correlational analysis and theoretical
discussion, it is concluded that, although no significant differences were identified in the use
48
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
and perception of AI according to sociodemographic variables, structural and institutional
limitations persist that hinder its effective integration. The triangulation of the findings
allows us to affirm that the positive impact of artificial intelligence in higher education
depends not only on its technological availability, but also on an educational ecosystem that
integrates curriculum, ethics, teacher training, and institutional policies. Consequently,
artificial intelligence is consolidated as a strategic resource for higher education in
Economics, provided that its implementation is carried out in a critical, ethical, and
pedagogically sound manner.
Bibliographic references
Armijos-Saca, R. A., Gutiérres-Vargas, M. M., Crespo-Gordillo, B. Z., Espinoza-Agila, L. E., &
Púa-Pilay, J. E. (2025). Artificial intelligence (AI) in the personalization of classroom
learning [La inteligencia artificial (IA) en la personalización del aprendizaje en el
aula]. Ciencia Latina Revista Científica Multidisciplinar, 9(1), 7590–7601.
https://doi.org/10.37811/cl_rcm.v9i1.16427
Baltazar, C. (2023). AI tools applicable to education [Herramientas de IA aplicables a la
educación]. Technology Rain Journal, 2(2), 1–14.
https://doi.org/10.55204/trj.v2i2.e15
Biggs, J. B. (1987). Study Process Questionnaire manual: Student approaches to learning and
studying. Australian Council for Educational Research.
Espinales-Franco, J. S., Pazmiño-Campuzano, M. F., & Zambrano-Acosta, J. M. (2024).
Artificial intelligence as an innovative teaching tool in higher education: The case of
Universidad Técnica de Manabí [Inteligencia artificial como herramienta innovadora
de enseñanza en la educación superior. Caso: Universidad Técnica de Manabí].
Revista Multidisciplinar Arbitrada de Investigación Científica, 8(3), 4729–4748.
https://doi.org/10.56048/MQR20225.8.3.2024.4729-4748
Espinosa-Vallejo, J., Solís-Naranjo, L., Constante-Portero, D., Constante-Portero, M., Criollo-
Cárdenas, N., & Martínez-Ruiz, E. (2025). Artificial intelligence applied to education:
Pedagogical innovation, digital inclusion, and learning transformation [La
inteligencia artificial aplicada a la educación: innovación pedagógica, inclusión
digital y transformación del aprendizaje]. Revista de Estudios Generales, 4(3), 870–
892. https://doi.org/10.70577/reg.v4i3.203
Gómez-Cárdenas, R., Fuentes-Penna, A., & Castro-Rascón, A. (2024). Ethical and moral use of
artificial intelligence in education and research [El uso ético y moral de la inteligencia
artificial en educación e investigación]. Ciencia Latina Revista Científica
Multidisciplinar, 8(5), 3243–3261. https://doi.org/10.37811/cl_rcm.v8i5.13801
González-Alarcón, N., & Melguizo, A. (2023). Skills for a future workforce with generative AI:
Knowledge and strategies [Habilidades para un futuro laboral con IA generativa:
conocimientos y estrategias]. UNESCO.
https://www.unesco.org/es/articles/habilidades-para-un-futuro-laboral-con-ia-
generativa-conocimientos-y-estrategias
Jardón, M. del C., Granizo, J. H., Yaselga, W. F., & Cocha, M. G. (2024). Impact of artificial
intelligence virtual assistants on university students’ academic performance [Impacto
49
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
de los asistentes virtuales de inteligencia artificial en el rendimiento académico de
estudiantes universitarios]. Revista Social Fronteriza, 4(4), 1–27.
https://doi.org/10.59814/resofro.2024.4(4)338
Lamas-Lara, C. A., Martinez-Del Rio, I. P., Beltran-Pineda, L. B., & Romero-Vela, S. L. (2025).
Research-based learning: Development of critical thinking in higher education
[Aprendizaje basado en investigación: desarrollo del pensamiento crítico en
educación superior]. Horizontes. Revista de Investigación en Ciencias de la Educación,
9(40), 410–426. https://doi.org/10.33996/revistahorizontes.v9i40.1152
Lima, G. J., Fernández, J. A., Cruz, O. J., & Guriz, N. O. (2025). Application of artificial
intelligence for personalized learning in university environments: Challenges,
opportunities, and impact on academic performance improvement [Aplicación de la
inteligencia artificial para la personalización del aprendizaje en entornos
universitarios: desafíos, oportunidades y su impacto en la mejora del rendimiento
académico]. Revista Social Fronteriza, 5(4), 1–19.
https://doi.org/10.59814/resofro.2025.5(4)786
Ludeña-Yaguana, J. E., Lozada-Monsalve, K. L., Calle-Landazuri, D. M., & Chiza-Yamberla, E.
J. (2025). Personalized pedagogy with artificial intelligence: A study on its impact on
student performance and motivation [Pedagogía personalizada con inteligencia
artificial: Un estudio sobre el impacto en el rendimiento y la motivación estudiantil].
Revista Científica Multidisciplinaria HEXACIENCIAS, 5(10), 224–245.
https://soeici.org/index.php/hexaciencias/article/view/700
Menacho-Ángeles, M., Pizarro-Arancibia, L., Osorio-Menacho, J., Osorio-Menacho, J., & León-
Pizarro, B. (2024). Artificial intelligence as a tool in autonomous learning among
higher education students [Inteligencia artificial como herramienta en el aprendizaje
autónomo de los estudiantes de educación superior]. INVECOM, 4(2), 1–9.
https://revistainvecom.org/index.php/invecom/article/view/3142
Morocho-Pintag, J., Yaselga-Auz, W., Lizano-Jácome, M., & Medina-Romero, M. (2025).
Digital and AI competencies in education: Transforming students to lead the future of
work [Competencias digitales y de IA en la educación: transformando a los
estudiantes para liderar el futuro del trabajo]. Reincisol, 4(7), 2841–2864.
https://doi.org/10.59282/reincisol.V4(7)2841-2864
Numa-Sanjuán, N., Diaz-Guecha, L. Y., & Peñaloza-Tarazona, M. E. (2024). Importance of
artificial intelligence in 21st-century education [Importancia de la inteligencia
artificial en la educación del siglo XXI]. Revista de Investigación, Administración e
Ingeniería, 12(2), 49–62. https://doi.org/10.15649/2346030X.3776
Palma-Landirez, K. G., Feijoo-Romero, O. S., & Rumbaut-Rangel, D. (2024). Impact of artificial
intelligence on the academic performance of third-year high school students [Impacto
de la inteligencia artificial en el rendimiento académico de los estudiantes de tercer
año de bachillerato]. Revista Multidisciplinar de Investigación Científica, 8(2), 4012–
4025. https://doi.org/10.56048/MQR20225.8.2.2024.4012-4025
Peñaherrera, W. P., Cunuhay, W. C., Nata, D. J., & Moreira, L. E. (2022). Implementation of
artificial intelligence (AI) as an educational resource [Implementación de la
50
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
inteligencia artificial (IA) como recurso educativo]. Revista Científica Mundo de la
Investigación y el Conocimiento, 6(2), 402–413.
https://doi.org/10.26820/recimundo/6.(2).abr.2022.402-413
Piedra-Castro, W. I., Cajamarca-Correa, M. A., Burbano-Buñay, E. S., & Moreira-Alcívar, E. F.
(2024). Integration of artificial intelligence in the teaching of social sciences in higher
education [Integración de la inteligencia artificial en la enseñanza de las Ciencias
Sociales en la educación superior]. Journal of Economic and Social Science Research,
4(3), 105–126. https://doi.org/10.55813/gaea/jessr/v4/n3/123
Pita-Briones, K., Jiménez-Pin, K., Saldarriaga-Alvarado, I., & Meneses-López, S. (2025).
Teachers’ digital competencies in the face of educational artificial intelligence
[Competencias digitales docentes frente a la inteligencia artificial educativa]. 593
Digital Publisher CEIT, 10(5), 900–916.
https://doi.org/10.33386/593dp.2025.5.3569
Ponce-Tituaña, L. G., Quelal-González, N. M., Tupiza-Cumbal, M. del P., & Verduga-Shiguango,
H. A. (2025). Teachers’ digital competencies in higher education: Assessment,
challenges, and strategies for institutional strengthening [Competencias digitales
docentes en la educación superior: evaluación, desafíos y estrategias para su
fortalecimiento institucional]. Multidisciplinary Journal of Sciences, Discoveries, and
Society, 2(3), e-226. https://doi.org/10.71068/r2eawg98
Ribas, F. B., & Provasi, M. R. (2024). Adoption of generative artificial intelligence: Perceptions
of economics students [Adopción de inteligencia artificial generativa: percepciones
de alumnos de ciencias económicas]. In XX Simposio Regional de Investigación
Contable (pp. 2683–6734). http://sedici.unlp.edu.ar/handle/10915/175480
Rochina, S., Duarte, M., Macanchí, M., & Tipantuña, E. (2024). Educational transformation in
the 21st century: Integration of emerging technologies for effective learning
[Transformación educativa en el siglo XXI: Integración de tecnologías emergentes
para el aprendizaje efectivo]. Reincisol, 3(6), 6092–6109.
https://doi.org/10.59282/reincisol.V3(6)6092-6109
Rodríguez-Martínez, A., Jiménez-Delgado, J. J., & Anta-Félez, J. L. (2025). The digital divide:
Technology and humanity [La brecha digital. Tecnología y humanidad]. Sociedad &
Tecnología, 8(S1), 4–17. https://doi.org/10.51247/st.v8i2.557
Rosero, J. R., & Guevara, D. A. (2025). Exploring the use of artificial intelligence to improve
the efficiency of LMS virtual e-learning environments for secondary education
[Exploración del uso de la inteligencia artificial en la eficiencia de entornos virtuales
LMS de e-learning para la educación secundaria]. Ciencia Latina Revista Científica
Multidisciplinar, 8(6), 6134–6151. https://doi.org/10.37811/cl_rcm.v8i6.15310
Ruiz-Miranda, E. (2023). The artificial intelligence revolution in education: A review of
ChatGPT [La revolución de la inteligencia artificial en la educación: una reseña de
ChatGPT]. Revista de Estudios e Investigación en Psicología y Educación, 10(1), 156–
160. https://doi.org/10.17979/reipe.2023.10.1.9594
51
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Sánchez-Salazar, T. R., Gómez-Alcívar, V. J., Henríquez-Carrera, E. G., & Melecio-Arana
Cadena, R. (2025). Use of artificial intelligence as a tool to enhance comprehensive
training of university students: Opportunities and challenges [Uso de la inteligencia
artificial, herramienta para potenciar la formación integral de los estudiantes
universitarios: oportunidades y desafíos]. Journal of Science and Research, 9.
https://revistas.utb.edu.ec/index.php/sr/article/view/3464
Vélez-Rivera, R., Muñoz-Álvarez, D., Leal-Orellana, P., & Ruiz-Garrido, A. (2024). Use of
artificial intelligence in higher education and its ethical implications: A systematic
mapping of the literature [Uso de inteligencia artificial en educación superior y sus
implicancias éticas. Mapeo sistemático de literatura]. Hachetetepé: Revista Científica
de Educación y Comunicación, (28), 1–17.
https://doi.org/10.25267/Hachetetepe.2024.i28.1105
Vera, F. (2023). Integration of artificial intelligence in higher education: Challenges and
opportunities [Integración de la inteligencia artificial en la educación superior:
desafíos y oportunidades]. Revista Electrónica Transformar, 4(1), 17–34.
https://www.revistatransformar.cl/index.php/transformar/article/view/84
Authors
SANTIAGO VINUEZA-VINUEZA earned his PhD in Educational Research from César Vallejo
University in Peru in 2025, a Master's degree in Communication Networks from the Faculty
of Engineering at the Pontifical Catholic University of Ecuador in 2016, a Master's degree in
Educational Information Systems from Israel Technological University in 2009, a Bachelor's
degree in Education with a specialization in Computer Science from the Faculty of
Philosophy, Letters, and Educational Sciences at the Central University of Ecuador in 2002,
and a Bachelor's degree in Computer Engineering from the Autonomous University of Quito
in 2002.
He is currently a full-time professor in the Faculty of Economic Sciences at the University of
Ecuador. His main research focuses on education and Information and Communication
Technologies. He is the author of book chapters and articles published in high-impact
journals (Emerging Sources Citation Index, Scopus, Latindex, Redalcy, SciELO).
ALEJANDRA FONSECA-FACTOS earned her Master's degree in Education with a
specialization in Innovation and Educational Leadership from Indoamerica University
(Ecuador). She also holds a degree in Electronics and Telecommunications Engineering
from the University of the Armed Forces-ESPE.
Currently, she teaches both the regular and supplementary technical high school programs
at the Uyumbicho Educational Unit in Mejía Canton, Pichincha Province. She has served as
a peer reviewer for the journal Conectividad-Rumiñahui University Institute. She
collaborates with the Ministry of Education, Sports, and Culture, providing technical and
pedagogical support for the implementation of educational robotics projects in schools
within the education system. Her main research interests include STEAM approaches,
educational robotics, educational innovation, the didactics of exact and natural sciences
(physics), biomedicine, and technologies applied to education. She is the author of several
articles published in conferences and high-impact journals (IEEE Xplore, Scielo, Latindex,
DOAJ).
52
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 9(1), pp. 35-52, January-June 2026. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v9i1.9432
Declaration of authorship-CRediT
SANTIAGO VINUEZA-VINUEZA: State of the art, related concepts, methodology, validation,
data analysis, writing – first draft.
ALEJANDRA FONSECA-FACTOS: State of the art, related concepts, data analysis,
organization and integration of collected data, conclusions, final writing and editing.
Declaration of the use of artificial intelligence
The authors declare that they did not use Artificial Intelligence (AI) tools for any part of the
manuscript. All material was reviewed and validated by the authors, who are responsible
for its accuracy and rigor.
