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Development of research competencies
through artificial intelligence. An innovative
approach
Desarrollo de competencias investigativas a través de la
inteligencia artificial. Un enfoque innovador
Kléver Cárdenas- Velasco
Universidad Nacional de Rosario, Santa Fe, Argentina
Facultad de Humanidades y Artes, Doctorado en Educación
kgcardenas@uce.edu.ec
https://orcid.org/0000-0002-4070-6361
Jesenia Moreira-Benavides
Universidad Central del Ecuador, Quito, Ecuador
Facultad de Filosofía, Letras y Ciencias de la Educación, Carrera de Psicopedagogía
jmmoreirab@uce.edu.ec
https://orcid.org/0000-0003-2701-5168
Celia Amores-Pacheco
Universidad Central del Ecuador, Quito, Ecuador
Facultad de Filosofía, Letras y Ciencias de la Educación, Carrera de Psicopedagogía
cramores@uce.edu.ec
https://orcid.org/0000-0003-0319-1693
Mariela Núñez-Santiana
Universidad Técnica de Ambato, Ambato, Ecuador
Facultad de Ciencias Humanas y de la Educación, Carrera de Educación Básica
mariela.nunez@educacion.gob.ec
https://orcid.org/0009-0004-2608-1061
(Received on: 08/03/2024; Accepted on: 20/07/2024; Final received on: 18/09/2024)
Suggested citation: Cárdenas-Velasco, K., Moreira-Benavides, J., Amores-Pacheco, C. y
Núñez-Santiana, M. (2025). Development of research competencies through artificial
intelligence. An innovative approach. Revista Cátedra, 8(1), 18-38.
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Abstract
The article presents the topic on the Development of research competencies through
artificial intelligence. An innovative approach. It responds to the constructivist paradigm
that focuses on active learning based on the context of experiences and cognetivism,
proposes knowledge networks with technology management for the construction of
learning. Ideas are described to help solve the current problem, since teachers and students
do not have sufficient knowledge of Artificial Intelligence (AI) and its relationship with
research competencies. The objective is to propose the optimization of the educational
process with the incorporation of AI and the development of research skills in students. It
is based on updated contents on the two main topics, with emphasis on their ethical
dimension. The analytical-synthetic method was used; documentary analysis and
bibliographic review with specialized scientific information that supports the work and
projects its results to other possible studies. It is concluded that there is a need to
incorporate AI progressively in educational institutions, in which the necessary virtual
support should be installed so that research competences are applied in the learning
process and can be efficiently managed by teachers and gradually by students. It is an
indispensable requirement to achieve a qualitative improvement in the educational
processes in order to leave aside verbalism and give way to the formation of cooperative
groups in which students raise their concerns, seek alternative solutions and find their own
answers.
Keywords
Learning, research skills, innovative approach, ethics, artificial intelligence.
Resumen
El artículo presenta el tema sobre el Desarrollo de competencias investigativas a través de
la inteligencia artificial. Un enfoque innovador. Responde al paradigma constructivista que
enfoca el aprendizaje activo fundamentado en el contexto de experiencias y el cognitivismo,
plantea redes de conocimiento con manejo de tecnología para la construcción del
aprendizaje. Se describen ideas que ayuden a resolver el problema actual, pues, los docentes
y estudiantes no tienen el suficiente conocimiento de la Inteligencia Artificial (IA) y su
relación con las competencias investigativas. El objetivo es proponer la optimización del
proceso educativo con la incorporación de la IA y el desarrollo de competencias
investigativas en los estudiantes. Se sustenta en contenidos actualizados sobre los dos
temas principales, con énfasis en su dimensión ética. Se empleó el método analítico-
sintético; el análisis documental y la revisión bibliográfica con información científica
especializada que respalda el trabajo y proyecta sus resultados a otros posibles estudios. Se
concluye que existe la necesidad de incorporar la IA de manera progresiva en las
instituciones educativas, en las cuales se deberá instalar el soporte virtual necesario para
que se apliquen las competencias investigativas en el proceso de aprendizaje y lo puedan
manejar eficientemente los docentes y paulatinamente los estudiantes. Es un requisito
indispensable para lograr un mejoramiento cualitativo en los procesos educativos con la
finalidad de dejar a un lado el verbalismo y dar paso a la conformación de grupos
cooperativos en los cuales los alumnos planteen sus inquietudes, busquen alternativas de
solución y encuentren sus propias respuestas.
Palabras clave
Aprendizaje, competencias investigativas, enfoque innovador, ética, inteligencia artificial.
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1. Introduction
This article discusses the issue of developing research competencies through Artificial
Intelligence (AI). An innovative approach. In recent years, AI has made inroads in various
fields, the problem is that education cannot remain on the sidelines of this benefit, both
students and teachers must take on this innovative challenge to achieve new styles of
learning and teaching.
The present study aims to propose the optimization of the educational process with the
incorporation of AI and the development of research skills in students. Students will
construct their own learning through the use of collaborative work, a strategy that gradually
develops in them self-management skills and competencies. AI is the justification for
carrying out this program. González states that AI itself is capable of providing feedback
and acquiring new capabilities such as predicting behaviors and interests. This is achieved
The process facilitates the application of didactic strategies so that teachers can plan in a
more dynamic way, and become guides for students who must incorporate research skills
through the management of new and interesting alternatives to problem situations. This
way of acting prepares them so that in their future life they will be ready to find alternative
solutions based not only on knowledge, but also on practical experiences developed in
interrelated work. It can be affirmed that the article responds to the relevance of the study
from some points of view: it facilitates the change of attitudes of teachers and students, it
allows the incorporation of strategies, it opens spaces to incorporate computational tools,
it looks at the development of a university career with new perspectives, the future
graduates have at their disposal tools that will allow them to initiate and permanently
improve their professional actions.
The article is structured as follows: section 1. Introduction, synthesizes the aspects
contained in the article; section 2. Literature review, explains the theoretical and
methodological elements of AI and the competency-based approach in education; section 3.
Methods and Materials, focuses on the methodological processes and instruments used;
section 4. Results, describes the authors' proposals; section 5.
2. Literature review
2.1 Artificial Intelligence
AI represents a breakthrough in the technological revolution, linking human dexterity with
the power of machines to solve complex problems. It applies innovative solutions that
transform the way technology and the surrounding world interact. Rouhiainen (2018)
-Ahuerma
learning technologies to give machines the ability to apply certain cognitive skills and
perform tasks on their own autonomously or semi-96). AI devices, unlike
humans, do not need to rest and can analyze extensive information at once. In its constant
evolution, it opens new frontiers in the understanding and application of intelligence,
overcomes traditional limitations and rethinks the landscape of what is possible.
In the last decades worldwide, the application of AI in the development of learning
processes in educational systems has been growing. This reality is also observed in Ecuador,
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where the aspiration to generalize its practice in school classrooms is still scarce as
explained by research conducted by (Ashford-Rowe, et al., 2019; Frontier Economics, 2018;
Ganascia, 2018; Luckin et al., 2016; OECD, 2018). The use of AI in education becomes a
challenge for teachers and students who must acquire competencies at two levels: technical
with specific tools in digital development, and methodological with innovative strategies for
the didactic process. The application of AI at all levels of the education system will have the
on what new AI technologies can bring to reduce access barriers, automate management,
the implementation of AI requires specialized planning, monitoring, and ongoing evaluation
to ensure that the objectives of each level of study are met. To ensure educational policies,
programs and practices, it is essential that AI be applied and have a positive impact on the
learning and development of students with investigative competencies, oriented to build
scientific and technological thinking.
2.2 Artificial intelligence in education
The incorporation of AI in education is intended to promote the improvement of the quality
of educational institutions with the improvement of learning outcomes through
technologies and techniques planned by specialists in the organization of the curriculum of
seeks that computers, machines and other artifacts emulate human intelligence, thus
developing learning and adaptability skil
(p. 1). This purpose is beginning to be felt as a need to be incorporated progressively by
teachers, who are being demanded because some students are beginning to apply it for their
school work only as a copy and paste.
The application of AI in classrooms requires teachers and students to develop, in advance,
a set of basic skills on machine learning algorithms, evaluation and questioning of query
results, identification of biases and limitations, programming to customize research results,
selecting and classifying data according to topics, management of platforms related to
learning content, always in a responsible and ethical manner (Espinoza et al., 2023; Holmes
et al., 2021).
The assessment of learning outcomes among teachers and students will increase its
application in other educational institutions that wish to keep up with the latest trends and
technological advances with AI management (Cruz et al., 2023; Pastora and Fuentes, 2021;
Vivar and Peñalo, 2023). Its possibilities will increase with practice. As Sandoval (2018)
and artificial intelligence will demand educators to be able to respond to the needs, interests
university as responsible for linking the learning acquired at school with the requirements
of the working world should integrate the subject of AI in its curricula (Moreno-Gutiérrez
et al., 2022). It is the task of teachers to learn about AI so that it becomes a tool to support
the improvement of the quality of education.
2.3 Optimization of learning with artificial intelligence
Intelligence (AI) is a reality that surpasses fiction in many aspects,
Rojas, 2021, p. 504). Its application as a technical tool that optimizes learning requires
specialized planning and specific training of each pedagogical process for teachers and
students to apply this academic advance successfully. Its implementation will allow policies,
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programs, projects and educational practices to influence the learning and development of
students with research competencies aimed at building scientific and technological
thinking. The institutions that apply it socialize strategies with better learning results
nt, from the most initial
ones, in which there is not some participation or interaction with a computer device that
These include: personalization of learning, personalized virtual assistants, virtual tutoring,
data analytics, simulations and virtual labs, online collaboration, knowledge gap detection,
adaptive gamification, formative assessment (Del Puerto and Esteban, 2022; Martínez-
Comezaña et al., 2023).
The progress of AI, considered as a scientific discipline, is in a diffusion stage. This situation
predicts that, in education, its influence will be greater due to the socialization among
teachers who have applied it, who, among others, suggest the following platforms as
supports that can be useful and optimize their educational practices, according to each level
of studies and for an inclusive education.
General Basic Education: DreamBox interactive math lessons, Knewton real-time content
and assessments based on student performance, IXL Learning practice activities in math,
reading, writing, science and other subjects, Prodigy educational math game, Edmentum,
SMART Learning Suite Online, ALEKS, DreamBox Learning, Quizbot, Skills Strand activities
in different areas, Fishtree, Mindspark intelligent tutor focused on reading and math
(García-Cosio, 2021; Rivas, 2018). These tools are used by teachers according to the
progress of curricular content as follow-up actions related to the progress of each student.
They can be socialized to parents to work at home as support for their children.
General Unified Baccalaureate: Code.org with programming, AI4K12 for machine
learning, Google AI Experiments with hands-on interactive projects, IBM Watson Education,
for project-based learning creates simple chatbots or programs that make decisions based
on data, digital competencies to search for information, evaluate sources and maintain
privacy and online collaboration among students with secure platforms, visits to AI
technology companies. These are platforms with valuable educational experience for
students (Abeliuk, 2023; Coicaud, 2019; Lavanda-
Intelligence promises the improvement of education on a large scale, with the main feature
of personalization according to -Moles, 2021, p. 15).
Higher Education: Khan Academy adapts mathematics content and other subjects to the
individual needs of students, Coursera offers online courses, Edmodo teaches social
learning modalities, DreamBox with mathematical processes for primary and helps pre-
professional practices, Adaptive Learning Systems has personalized adaptive learning
systems, IBM Watson Education personalizes content and assessments, Blackboard Learn
suggests content and activities, Google Classroom and Google Workspace for Education,
personalize E-A, Symbaloo creates personalized resource dashboards (Salmerón, et al.,
2023; Ocaña-Fernández et al., 2019; Quiroz, 2023). These platforms help both students and
teachers in their interrela
-Navarro et al., 2023, p. 105).
Inclusive education. At all levels of the education system, IA supports teachers with a set
of strategies for educational inclusion, to achieve personalized learning and ensure that
students with special educational needs work with the whole group without discrimination
and with self-oduce
inclusion as an educational model. It is based on understanding to forge an interaction with
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2017, p. 13).
In inclusive education, educators use AI as a tool to incorporate the whole group into the
work. It helps teachers to customize content and strategies according to the individual
learning pace of each student, especially those with special educational needs. AI leads to
increased student engagement with improved learning outcomes. The integration of
cognitive, affective and social characteristics contributes progressively to improve
academic performance (Fernandez, 2023; Jara and Ochoa, 2020; UNESCO, 2019).
In inclusive education, platforms offer interactive activities and exercises that are adapted
difference is to seek meeting points where diversity can collectively creat
Riveros, 2021, p. 354). The pedagogical strategies that teachers can implement to take
advantage of them as learning tools, among others are, personalized tutorials. Simulations
and virtual laboratories on science or historical recreations. Individual assessment of each
student to identify learning strengths and weaknesses, in real time. Intelligent educational
games that reinforce the concepts of the different subjects. Automated grading of essays,
quizzes and homework assignments. Virtual classroom assistance that answers student
questions. Computer vision management to analyze errors, give automated feedback with
video tutorials, exercises and practice tests. Encouragement of discussion and writing to
generate creative texts. Exploration of complex concepts to explain abstract topics (Tarrillo-
Flores, 2022, p. 24-33).
To conclude, AI influences the transformation of education, offering opportunities to
improve the quality and accessibility of learning. These systems can personalize educational
approaches, adapting them to the individual needs of students. AI achieves more effective
and efficient learning because it analyzes the performance and progress of each student
with personalized feedback and content tailored to learning skills and preferences.
2.4 Ethics and artificial intelligence in education
The application of AI as a work tool facilitates the interrelation between students and
teachers with various platforms, and constitutes a support for the pedagogical processes of
different learning subjects; however, it is important to be cautious about its use, as
explained by Flores-Vivar and García-Peñalvo (2023).:
Information and communication technologies, represented through
networks and social media, knowledge-based systems, interactive
multimedia, big data and artificial intelligence, are an intrinsic part of the
social fabric, (...) of the disciplines of knowledge, playing an increasingly
important role that will even increase in the future. Its presence is
omnipresent in education, so the development, evolution and expansion
of these technologies in the educational context, (...) requires in-depth
and comprehensive studies that show the advantages and disadvantages
of their use (p. 1).
The use of AI in education is inevitable because of its ability to support academic
advancement. It is imperative that teachers and students value its application from ethical
and moral perspectives. It is essential to prevent students from mechanically copying
content without reflection just to accomplish a task. This attitude will result in superficial
learning without acquisition of new knowledge and will cause students to pass from one
year to another without skill development, which not only contributes to educational
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inequality, but also threatens equity in access to education (Piedra et al., 2023; Llovera-
López et al., 2023; Naupay-Gusukuma, 2023; Rivas, 2018; Del Campo et al., 2023).
Given this reality, teachers are encouraged to prioritize strategies that foster critical
thinking and reflection in students when using AI as an educational tool. According to García
et al. also highlights the importance of incorporating ethical values in the educational
sense that guiGarcía et al.,
2023).
3. Research Competencies
earch
and selection of scientific information according to the problems they are developing. Use
of technological tools that facilitate the systematization of issues of interest for their
research processes. Knowledge of the scientific method to adapt their processes to this
methodology. Application of techniques for the collection of information. Elaboration of
research results. Formulation of pertinent and relevant conclusions. Teamwork to better
ensure research results (Chávez-Vera et al., 2022, p. 253).
3.1 Research competencies related to artificial intelligence
The aforementioned competencies and others that will emerge in the development of
student training should be supported with the use of AI, which in short is the use of
enter the stored information and ask questions about issues of interest, with the confidence
that the answers will be accurate. Hence, this article insists that the use of AI in education is
a challenge for both key actors, teachers and students, who have to acquire competencies
related to digital development and innovative strategies for the didactic process. In this
context, the learning of research competencies is urgent and necessary because it satisfies
the two mentioned requirements.
Cruz (2021) states that resea
identifying, interpreting, arguing, and solving problems of the context with suitability and
40). It refers to the fundamentals that a researcher should handle and that he/she should
do so in an ethical environment. Research competencies will be developed from different
approaches: conceptual, accompanying and delimiting, and socio-formative. In the
conceptual approach, it refers to a process that facilitates the solution of problems of reality,
for which teachers and students must articulate collaborative work to a life project with
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ethical values. In the accompaniment approach, emphasis should be placed on the formation
of innovative responses to specific problem situations in the context in which they develop.
In the delimitation approach, they are prepared to respond in the future to a job position.
of forming people capable
of facing diverse challenges of their context with creativity, good disposition, attitude of
-
use different information and communication technologies through networks and social
media. They must avoid using ambiguous, vague or misleading language, and develop
-Gusukuma, 2023, p. 17). It can
be inferred that the obligation of students is to face real situations, before which they must
bring out the competences acquired in the learning process, which in the particular case of
research competences are still insufficient.
With reference to higher education Ceballos-Almerayaya expresses that
the future teacher as a researcher in training must develop research
competencies that allow him/her to act in new situations, have the ability
to identify, pose and solve problems, under a commitment with his/her
sociocultural environment, increase skills to work in international
contexts, search, process and analyze information (Ceballos-Almerayaya,
2021, p. 183).).
3.2 Evaluation of research competencies in research projects
resort to the use of rubrics because competencies constitute the set of
comprehensive actions that allow for solutions to real-world problems in
a holistic manner, which impacts the adoption of multidisciplinary work
and peer collaboration (p. 64).
that digital competencies in the area of communication and collaboration
are not dependent on the student's academic level or gender, but rather
on the family's economic and cultural level, which facilitate or hinder
access to digital devices for acquiring knowledge (p. 16).
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3.3 Research competencies in the educational curriculum
4. Methods and materials
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1_Identification and search for articles with the review of literature and
previous research to highlight the key competencies to be developed, and
design teaching and learning strategies for all levels of the educational
system, based on the use of AI. 2_ Evaluation of the quality of evidence to
accept, improve, or reject the authors' submissions, through the selection
of the most suitable tools for the development of research competencies
with the support of AI. 3_ Synthesis of the articles with the analysis of the
results to identify patterns, trends, practice proposals, and conclusions
that contribute to scientific knowledge in the field of development of the
topic proposed in the article (p. 14).
5. Results
The results of the literature review and conducted research support the proposal of the
researchers who advocate for the need to incorporate AI supported by research
competencies into the Ecuadorian educational system. It is anticipated that its application
will produce, among other things, the following changes in all curricular elements and in
their implementation by the human talent of educational institutions.
5.1 In aspects related to the curriculum
The improvement of the quality of education through the application of AI requires the
technological training of all actors in the educational system because its practice will impact
educational curricula, which will be adapted to the context with the participation of the
entire educational community in new teaching and learning models. The following are
presented in two matrices the most relevant aspects of the results concerning the
curriculum (table 1) and human talent (table 2).
Fluent
communication in
a foreign language
Chabot
management
Educational
problem solving
skills
Increased
cognitive skills
Use of virtual
resources in which
the mastery of basic
foreign language
skills, especially
English, is required.
Chabots are
software based on
Artificial
Intelligence, capable
of maintaining a
real-time
conversation by text
or voice. A great
opportunity to see
the importance of
AI.
In the different
subjects of the
educational
curriculum, the
teacher can
organize
collaborative work
groups for the
students
themselves to solve
and find creative
answers, with the
help of artificial
intelligence.
With the help of
artificial
intelligence and the
guidance of a
participatory and
purposeful work of
students, they can
find the opportunity
to develop cognitive
skills that favor the
development of
essential
competencies:
critical thinking,
cooperation and
adaptability.
Cuadro 1. Resultados curriculares
Learning, mastering and fluent communication of a foreign language. In language
teaching-learning processes, intelligent tutoring systems offer tools with instant and
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permanent feedback. The didactic interaction process will facilitate the execution of
communicative actions according to the learning contents with the use of virtual assistants.
Thus, in virtual learning training with the use of chatbots, AI provides personalized tutoring
and access to learning resources at any time, in addition to providing a more interactive and
accessible learning experience. These systems have shown to be effective in improving
students' motivation and commitment to accomplish tasks in individual and collaborative
group work. In improving educational problem-solving skills, AI tools are available in all
subjects in different years of study and allow the analysis of large amounts of educational
data to identify patterns and provide solutions to complex problems. Students in working
groups through simulation and scenario modeling learn to make educational decisions in
the face of study problems and explain with researched data each topic. In the increase of
cognitive skills with the development of research competencies, the application of AI will
contribute to link knowledge with research skills. These competencies will enable students
to receive, process and elaborate information autonomously with the presentation of
challenging tasks and the provision of instant feedback. AI facilitates the fulfillment of
holistic learning that encompasses not only knowledge acquisition, but also essential
competencies such as critical thinking, cooperation and adaptability. Studies show that AI
technologies can support autonomous learning and the development of metacognition in
students.
5.2 Aspects related to human talent
Its incorporation with the participation of trained teachers would prepare students from
the beginning of their education with skills that respond with knowledge, tools and
strategies appropriate to the digital transformation linked to the technological revolution
that support the scientific development of students. Its incorporation would allow the
improvement of some aspects related to:
Research and
critical thinking
Optimization of
digital
competencies
Inclusion of
students with SEN
Development of
interdisciplinary
projects
Development of
complex
investigations with
the analysis of data
corresponding to
varied experiences
and students apply
skills of
comprehension,
interpretation,
analysis, synthesis,
conclusion and
evaluation of their
learning.
Gradual acquisition
of digital tools and
devices that allow
students to find
information,
analyze their
results, elaborate
new proposals and
find their own
learning,
strengthening their
successes and
discarding their
mistakes.
With the support of
artificial
intelligence,
teachers can
incorporate their
students with
special educational
needs to fulfill their
learning with
special strategies so
that they do not feel
rejected.
Ejecución de
proyectos en los que
se pueda evidenciar:
colaboración
interdisciplinaria,
integración
tecnológica e
innovación, que
incluyan manejo de
hardware, software
y redes de
comunicación
eficientes.
Table 2. Human talent
The following is a more detailed explanation of the aspects listed in the matrix, with the
clarification that they are all supported by AI:
Personalization of learning. The teacher can modify traditional teaching schemes with
content planning, teaching-learning methods, evaluation processes with AI tools. Learning
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would be personalized by adapting it to the individual needs of urban and rural students
with results that would improve their performance and personal satisfaction. Teachers
would provide guidance so that different students learn to identify and address their
strengths and weaknesses in a more inclusive and effective way. It is worth noting its
valuable support for teachers who simultaneously serve several grades, single- and multi-
grade schools. Fostering research and critical thinking. AI tools can facilitate complex
research with the analysis of large volumes of data that respond to a variety of experiences.
The development of critical thinking and research skills could be objectified when teachers
observe that their students learn to access databases and advanced analytical tools, apply
skills of comprehension, interpretation, analysis, synthesis, conclusion and evaluation of
their learning. Improvement of digital competencies. The integration of AI in education
will prepare students for the application of devices, handling of computers, tablets, internet
browsing, searching for information on the web, interpretation and analysis of data,
graphics and statistics, management of tools to collect, clean and visualize data. These tools
prepare you for a future career with the management of advanced technologies to meet the
challenges of the 21st century.
In addition, the inclusion and accessibility of students with special educational needs
with the support of AI, teachers will be able to design understandable and personalized
learning tools for students with special needs that will allow them to automate learning.
This inclusion process would facilitate their incorporation into regular classrooms through
group work with collaborative learning strategies and would also prevent them from feeling
left out of the group and, sometimes, from being bullied. In the development of
interdisciplinary projects with AI tools with the management of AI, interdisciplinary
collaboration, technological integration and innovation projects would be carried out to
develop comprehensive and effective solutions that include the management of hardware,
software and efficient communication networks. These projects would not only allow
students to interact with advanced technologies, but would also help them develop critical
skills that would impact educational curricula with community decision making.
With reference to the development of interdisciplinary projects, the Organization of Ibero-
American States for Education, Science and Culture (OEI), in collaboration with the
ProFuturo Foundation, presents the following graph on the present and future relevance of
AI in terms of educational level (ProFuturo and OEI, 2023). Figure 1. shows the present and
future relevance of AI at the initial, primary, secondary and university levels. It can be seen
that, depending on the level, the students' skills management advances.
30
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Figure 1. Present and future relevance of AI. Source: (ProFuturo and OEI 2023)
It is anticipated that the integration of AI into the curriculum and training of human talent
can raise the quality of education by personalizing learning and providing adaptive
resources that fit the needs of each student by increasing knowledge retention and
academic performance.
6. Discussion and conclusions
predict student performance; create personalized lesson plans and
assessments tailored to their strengths and weaknesses; motivate
lifelong learning around the clock, through chatbot or virtual tutors,
machine learning, and other personal assistance tools; develop research
skills and prepare for the future professional to enter the workforce
(Abreu et al., 2021; Auqui, 2021; Iglesias-Gorrón, 2018; Rochín and
Anguiano, 2021; Zhang et al., 2019).
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it is necessary to consider in the educational system of all countries that
the ethical and social challenges associated with its implementation
address the digital divide, inequality of access to technology the need for
an appropriate balance between automation and human interaction,
because education is integral, it simultaneously develops simultaneously
the formation of cognitive, procedural, social and emotional skills of
students in their learning process (Aparicio-Gómez and Aparicio-Gómez,
2023; Hernández-Zuluaga, 2022; Rodríguez, 2022; Terrones-Rodríguez,
2018).
In conclusion, the promotion of research competencies not only makes possible, but also
favors the effective integration of artificial intelligence in the educational environment.
It is a true educational revolution that will transform teaching and
learning processes with intelligent educational resources that drive not
only quality improvement but also accessibility to knowledge on a
permanent basis. AI is a potential innovator with a variety of tools that
empower students, stimulate their creativity and cultivate critical
thinking. Fundamental elements for a transformative and progressive
education (Bernal-Segura, 2020; Carmona, Camacho et al., 2021; Rochín
and Anguiano, 2021; Rochín and Anguiano, 2021).
The research recognizes the need for teachers and students to continuously prepare
themselves not only to use this technology but also to develop optimal alternatives to
ensure educational quality and the preservation of human heritage through AI tools that
consider the challenges and ethical considerations to minimize risks in their educational
practice. It is the responsibility of the members of the educational community to create an
need for teachers and students
2020; Martínez-Comezaña et al., 2023; Terrones-Rodríguez, 2018; UNESCO, 2021).
In sum, it can be affirmed that what was established in the general objective of the research
work was fulfilled.
Acknowledgments
All human activity and the achievements obtained with it allow, even more, oblige to
recognize the people or institutions that facilitated its fulfillment. For this reason, on this
occasion we publicly acknowledge the Central University of Ecuador, the Alma Mater of
Ecuadorian Higher Education because it has been the academic space that allowed this
group of colleagues to carry out this proposal, providing us with the physical spaces and
academic support to crystallize the ideas that we have as a result of our teaching work.
Bibliographic references
Abeliuk, A. (2023). Sesgos algorítmicos en las redes sociales. Revista Científica UISRAEL,
15(1), 2-8.
Abreu, A., Castanho, R., Alisesa, J. & Carvalho, J. (2021). Transformación digital e innovación
tecnológica en la educación. Aranzadi/civitas.
Ashford-Rowe, K., Alexander, B., Barajas-Murph, N., Dobbin, G., Knott, J., McCormack, M.
& Weber, N. (2019). EDUCAUSE Horizon Report: 2019 Higher Education Edition.
32
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 8(1), pp. 18-38, January-June 2025. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v8i1.6621
Louisville, CO: EDUCAUSE. https://library.educause.edu/-
/media/files/library/2019/4/2019horizonreport.pdf
Abeliuk, A. (2023). Sesgos algorítmicos en las redes sociales. Revista Bits de Ciencia,
https://doi.org/10.71904/bits.vi24.12669
Abreu, A., Castanho, R., Alisesa, J. & Carvalho, J. (2021). Transformación digital e innovación
tecnológica en la educación. Aranzadi Thomson Reuters.
Aliaga-Pacora, A. A., Juárez-Hernández, L. G., & Herrera-Meza, R. (2021). Diseño y validez de
contenido de una rúbrica analítica socioformativa para evaluar competencias
investigativas en posgrado. Apuntes Universitarios, 11(2), 62 - 82.
https://doi.org/10.17162/au.v11i2.632
Aparicio-Gómez, O., & Aparicio-Gómez, W. (2023). Ética y educación en tecnología:
promoviendo la responsabilidad digital. Journal of alternative perspectives in the
social sciences, 12(1), 49 64.
https://www.japss.org/_files/ugd/cb6e00_ae30556501bd4c6a97d442e942106070
.pdf
Arana, C. (2021). Inteligencia artificial aplicada a la educación: Logros, Tendencias y
Perspectivas. INNOVA UNTREF. Revista Argentina de Ciencia y Tecnología, 1(7), 1-22.
https://revistas.untref.edu.ar/index.php/innova/article/view/1107
Arbeláez-Campillo, D., Villasmil-Espinoza, J., & Rojas-Bahamón, M. (2021). Inteligencia
artificial y educación humana: ¿Entidades contrapuesta o fuerzas complementarias?
Revista de Ciencias Sociales, 27(2), 502-513.
https://produccioncientificaluz.org/index.php/rcs/article/view/35937/38307
Ayuso, D. & Gutiérrez, P. (2022). La Inteligencia Artificial como recurso educativo durante la
formación inicial del profesorado. Revista Iberoamericana de Educación a Distancia,
25(2), 347-358. https://doi.org/10.5944/ried.25.2.32332
Bernal-Segura, J. (2020). Propuesta lineamientos de política pública: actualización de saberes
de los docentes de educación media acerca de TIC e inteligencia artificial para los
entornos rurales. [Tesis de maestría, Universidad de La Salle] Ciencia Unisalle
https://hdl.handle.net/20.500.14625/20407
Casillas-Martín, S., Cabezas-González, M. & Muñoz-Repiso, A. (2022). Influencia de variables
sociofamiliares en la competencia digital en comunicación y colaboración. Pixel- bit
Revista De Medios Y Educación 63, 7-33. https://doi.org/10.12795/pixelbit.84595
Ceballos-Almerayaya, J. (2021). Drive: un espacio virtual de investigación para el desarrollo
de competencias investigativas y digitales en pregrado. Panorama, 15(29), 82-102.
https://doi.org/10.15765/pnrm.v15i29.2537
Chávez-Vera, K., Ayasta-Llontop, L., Kong-Nunton, I., & Gonzales-Dávila-J. (2022). Formación
de competencias investigativas en los estudiantes de la Universidad Señor de Sipán
en Perú. Revista de Ciencias Sociales, 28(1). 250-260.
https://doi.org/10.31876/rcs.v28i1.37689
Coicaud, S. (2019). Potencialidades didácticas de la inteligencia artificial. Noveduc.
Cruz, M. T. (2021). Estrategias creativas para el desarrollo de Competencias
Investigativas en estudiantes de Maestría de una universidad privada. [Tesis
doctoral, Universidad Femenina Del Sagrado Corazón] Repositorio Institucional
UNIFÉ. http://hdl.handle.net/20.500.11955/859
Del Campos, G., Villota, W., Andrade, E. & Montero, Y. (2023). Análisis bibliométrico
sobre estudios de la neurociencia, la inteligencia artificial y la robótica: énfasis
en las tecnologías disruptivas en educación. Salud, Ciencia y Tecnología, 3, 362.
https://doi.org/10.56294/saludcyt2023362
Deroncele, A., Gross, R. & Medina, P. (2021). El mapeo epistémico: herramienta
33
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 8(1), pp. 18-38, January-June 2025. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v8i1.6621
esencial en la práctica investigativa. Universidad y Sociedad, 13(3), 172 - 188.
https://rus.ucf.edu.cu/index.php/rus/article/view/2088
Fernández, M. (2023). La Inteligencia Artificial en la Educación. Hacia un Futuro de
Aprendizaje Inteligente. Estudios Culturales Serie Educación y
Sociotecnociencia. https://doi.org/10.5281/zenodo.7823874
Fernández-Navas, M., Alcaraz-Salarirche, N., & Pérez-Granados, L. (2021). Estado y
problemas de la investigación cualitativa en educación: divulgación,
investigación y acceso del profesorado universitario. Archivos Analíticos de
Políticas Educativas, 29(46). https://doi.org/10.14507/epaa.29.4964
Figueroa, C., & Santillán, E. (2021). Software libre educativo en una cultura digital.
Qartuppi. https://www.qartuppi.com/2021/SOFTWARE.pdf
Flores, A., Manrique, Z., Morote, J., Rodríguez, C., Sakibaru, L. y Vizcarra, H. (2023). El
futuro de la educación: cómo la inteligencia artificial transformará el aula.
Editorial Mar Caribe. https://works.hcommons.org/records/9xrzz-2x429
Flores-Vivar, J., & García-Peñalvo, F. (2023). Reflexiones sobre la ética, potencialidades
y retos de la Inteligencia Artificial en el marco de la Educación de Calidad
(ODS4). Comunicar: Revista científica iberoamericana de comunicación y
educación (74), 37-47. https://doi.org/10.3916/C74-2023-03
Frontier Economics. (2018). The Impact of Artificial Intelligence on Work. Frontier
Economics. https://www.frontier-economics.com/media/q4lnsyv1/ai-and-
work-evidence-review-full-report.pdf
Ganascia, J. (2018). Inteligencia artificial: Entre el mito y la realidad. El Correo de la
UNESCO. Inteligencia Artificial: Promesas y Amenazas. UNESCO.
https://courier.unesco.org/es/articles/inteligencia-artificial-entre-el-mito-y-
la-realidad
García, J., Sakibaru, L., Ortega, Y., García, B., Guevara, Y., & Vargas, C. (2023). Inteligencia
artificial en la praxis docente: vínculo entre la tecnología y el proceso de
aprendizaje. Editorial Mar Caribe. https://doi.org/10.17613/vqt1-cp64
García-Cosio, M. (2021). Taller de capacitación a profesores de preparatoria sobre el uso
de las plataformas de matemáticas Webassing y ALEKS para la evaluación del
aprendizaje. [Tesis de maestría, Tecnológico de Monterey].
https://hdl.handle.net/11285/644256
González, A. (2017). ¿Qué es Machine Learning? Revista de
transformación digital.
https://www.revistatransformaciondigital.com/2017/04/03/que-es-machine-
learning/
Gough, D., Thomas, J. & Oliver, S. (2012).Clarifying differences between review
designs and methods. [Aclarar las diferencias entre los diseños y métodos de
revisión]. Systematic Reviews. 1(28), 2-9. https://doi.org/10.1186/2046-4053-
1-28
Hernández-Zuluaga, J. (2022). Concentración del mercado digital: condiciones políticas
y jurídicas en la actual economía de la información. Revista chilena de derecho
34
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 8(1), pp. 18-38, January-June 2025. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v8i1.6621
y tecnología, 11(1), 229-260. https://doi.org/10.5354/0719-
2584.2022.61871
Iglesias-Gorrón, R. (2018). Videojuegos, un recurso didáctico para nuestro sistema
educativo. [Tesis de maestría, Universidad de Valladolid]. BUCLE.
http://uvadoc.uva.es/handle/10324/33333
Jara, I., & Ochoa, M. (2020). Usos y efectos de la inteligencia artificial en educación. Banco
Interamericano de Desarrollo. http://doi.org/10.18235/0002380
Llovera-López, Y., Aragón-Carretero, Y. & Cano-Olivares, P. (2023). Ciberplagio
Académico entre el estudiantado universitario: un acercamiento al estado
actual de la temática (2017-2020).
Revista
Colombiana
de
Educación (87), 207-226. https://doi.org/10.17227/rce.num87-13143
Luckin, R., Holmes, M., Griffiths, & 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
Macías-Moles, Y. (2021). La tecnología y la Inteligencia Artificial en el sistema
educativo. [Tesis de maestría, Universital Joume I].
https://core.ac.uk/download/481436033.pdf
Martínez-Comezaña, M., Rigueira-Diaz, X., Larrañaga-Janeiro, A., Martínez-Torres, J.,
Ocarranza-Prado, I. & Kreibel, D. (2023). Impacto de la inteligencia artificial en
los métodos de evaluación de la educación primaria y secundaria: revisión
sistemática de la literatura. Revista de Psicodidáctica,
28(2), 93-103. https://doi.org/10.1016/j.psicod.2023.06.001
Miao, F. & Holmes, W. (2021). Foro internacional sobre IA y los futuros de la
educación, desarrollo de competencias para la era de la IA, 7 y 8 de diciembre de
2020: informe de síntesis. UNESCO.
https://unesdoc.unesco.org/ark:/48223/pf0000377251.locale=es
Morandín-Ahuerma, F. (2023). Principios normativos para una ética de la inteligencia
artificial. Consejo de Ciencias y Tecnología del Estado de Puebla.
https://philarchive.org/archive/MORRDC-2
Moreno-Gutiérrez, S., López, S., & García, M. (2022). Inteligencia artificial en e-learning
escenarios plausibles en Latinoamérica y nuevas competencias de egreso.
Revista Iberoamericana de Tecnologías del Aprendizaje, 17(1), 31-40.
https://doi.org/10.1109/RITA.2022.3149833
Naupay-Gusukuma, J. E. (2023). Habilidades investigativas universitarias aplicadas a
través de la inteligencia artificial. [Tesis de maestría, Universidad César
Vallejo]. https://hdl.handle.net/20.500.12692/120556
Ocaña-Fernández, Y., Valenzuela-Fernández, A., & Gano-Aburto, L. (2019). La
inteligencia artificial y sus implicaciones en la educación superior: Propósitos y
representaciones Revista de psicología educativa, 7(2), 536-568.
https://doi.org/10.20511/pyr2019.v7n2.274
35
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 8(1), pp. 18-38, January-June 2025. e-ISSN: 2631-2875
https://doi.org/10.29166/catedra.v8i1.6621
OECD. (2018). IA: Máquinas inteligentes, políticas inteligentes: Resumen de la
conferencia. OECD Digital Economy Papers. https://doi.org/10.1787/f1a650d9-
es
Ogosi, J. (2021). Chatbot del proceso de aprendizaje universitario: una revisión
sistemática. Revista de Investigación Científica y Tecnológica Alpha Centauri,
2(2), 29-43. https://doi.org/10.47422/ac.v2i2.22
Paiz-Recinos, M., Valladares Cerezo, C., Paz Cabrera, M., Recinos Cifuentes, S., González,
E. & Rodríguez, F. (2020). Investigación cuantitativa y cualitativa en Psicología.
Centro Universitario de Occidente.
Pastora, B., & Fuentes, A. (2021). La planificación de estrategias de enseñanza en un
entorno virtual de aprendizaje. Revista Científica UISRAEL, 8(1), 59-76.
https://doi.org/10.35290/rcui.v8n1.2021.341
Pérez, I. (2017). Herramientas para la inclusión: de la educación a la sociedad. Revista
de Educación Inclusiva, 10(2), 13-30.
https://revistaeducacioninclusiva.es/index.php/REI/article/view/307
Piedra, J., Salazar, I., Vílchez, C., Cortez, H., García, B. & Amaya, K. (2023). La Inteligencia
Artificial al servicio de la gestión y la implementación en la educación. Editorial
Mar Caribe. https://doi.org/10.31219/osf.io/z2y7c
Ponce, N. & Riveros, N. (2021). Construyendo inclusión a través del lenguaje: el valor
de la palabra en los espacios educativos. REXE: Revista de estudios y
experiencias en educación, 19 (41), 345-357.
https://www.rexe.cl/index.php/rexe/article/view/1029
ProFuturo & OEI. (2023). Informe GEM: Aprendizaje y transformación digital en
Iberoamérica. Profuturo education.
https://profuturo.education/noticias/profuturo-oei-gem-informe-consulta-
aprendizaje-transformacion-digital-iberoamerica/
Quiroz, V. (2023). Aplicaciones de la Inteligencia Artificial Aliadas en la enseñanza de
Matemáticas. Ciencia Latina Revista Científica Multidisciplinar, 7(4), 7454-
7467. https://doi.org/10.37811/cl_rcm.v7i4.7498
Ramírez-Ramírez, L., & Fernández De Castro, J. (2020). Entornos virtuales de
aprendizaje: usabilidad y alcance en la formación de competencias
profesionales del área educativa. Revista digital FILHA(22), 1-23.
http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1370
Rivas, A. (2018). Un sistema educativo digital para Argentina. Documento de trabajo.
CIPPEC-EDULING. https://www.cippec.org/wp-
content/uploads/2018/07/165-DT-Un-Sistema-Educativo-Digital-para-la-
Argentina.pdf
Rouhiainen, L. (2018). Inteligencia artificial: 101 cosas que debes saber hoy sobre
nuestro futuro. Alienta Editorial.
https://proassetspdlcom.cdnstatics2.com/usuaris/libros_contenido/arxius/4
0/39307_Inteligencia_artificial.pdf
Salmerón, Y. M., Luna, H. E., Murillo, W. G., & Pacheco, V. A. (2023). El futuro de la
Inteligencia Artificial para la educación en las Instituciones de Educación
36
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
Revista Cátedra, 8(1), pp. 18-38, January-June 2025. e-ISSN:2631-2875
https://doi.org/10.29166/catedra.v8i1.6621
Superior. Revista Conrado, 19(93), 27-34.
https://conrado.ucf.edu.cu/index.php/conrado/article/view/315
Sanabria-Navarro, J. R., Silveira-Pérez, Y., Pérez-Bravo, D. D., & Cortina, M. (2023).
Incidencias de la inteligencia artificial en la educación contemporánea.
Comunicar: Revista Científica de Comunicación y Educación, 31(77), 97-107.
https://doi.org/10.3916/C77-2023-08
Sandoval, E. (2018). Aprendizaje e inteligencia artificial en la era digital: implicancias
socio- pedagógicas ¿reales o futuras? Revista boletín REDIPE, 7(11), 155-171.
https://revista.redipe.org/index.php/1/article/view/626
Tarrillo-Flores, M. Y. (2022). Estrategias pedagógicas para el desarrollo de
competencias investigativas en los docentes: Revisión sistemática [Tesis doctoral,
Universidad Nacional de Trujillo].
https://hdl.handle.net/20.500.12692/85388
Terrones - Rodríguez, A. (2022). Inteligencia artificial sostenible y evaluación
ética constructiva. Isegoría, (67), 1-10.
https://doi.org/10.3989/isegoria.2022.67.10
Terrones-Rodríguez, A. (2018). Inteligencia artificial y ética de la responsabilidad.
Cuestiones de Filosofía, 4(22), 141-170.
https://doi.org/10.19053/01235095.v4.n22.2018.8311
UNESCO. (2019). Consenso de Beijing sobre Inteligencia Artificial y Educación.
https://unesdoc.unesco.org/ark:/48223/pf0000368303.locale=es
Varguillas, C., Brizuela, S., Villaroel, P. y Gallardo, D. (2021). Estrategias de aprendizaje
e integración Curricular en la Universidad Técnica Particular de Loja en
Ecuador. Revista Venezolana de Gerencia, 26(95), 818-831.
https://doi.org/10.52080/rvgluz.27.95.23
37
Licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0)
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Authors
KLÉVER CÁRDENAS-VELASCO, obtained the following degrees: Doctor in Education at the
National University of Rosario (Argentina) 2023. Master's Degree in International
Specialized Teacher Training, Specialty in Language and Literature at the Complutense
University of Madrid (Spain) 2017. Master in University Teaching and Educational
Administration at Universidad Tecnológica Indoamérica (Ecuador) 2014. Zootechnical
Veterinary Doctor at the State University of Bolivar (Ecuador) 2012. Bachelor of Science in
Education, Language and Literature at the Central University of Ecuador (Ecuador) 2003.
Teacher in Primary Education, Instituto Superior Pedagógico Juan Montalvo (Ecuador)
1990.
Currently works for the Ministry of Education of Ecuador, is also a teacher tutor of master's
thesis at the Universidad Politécnica Estatal del Carchi and teacher tutor of doctoral thesis
at the Universidad Nacional de Rosario - Argentina.
JESENIA MOREIRA-BENAVIDES, holds the following degrees: Master in Treatment of
Learning Difficulties, Universidad Central del Ecuador (Ecuador), 2013. Bachelor's Degree
in Educational Sciences, Educational Psychology and Guidance, Universidad Central del
Ecuador (Ecuador), 2005. Technologist in Computer Systems Analysis, Escuela Politécnica
Nacional (Ecuador), 2002.
Currently, she is a professor in the Psychopedagogy program at the Universidad Central del
Ecuador.
CELIA AMORES-PACHECO, her academic background is: Master's Degree in Sexual Abuse
Prevention at the Pontificia Universidad Católica del Ecuador (Ecuador) (in progress).
Diploma in Safeguarding at the Gregorian University (Italy) 2023. Master in Vocational
Discernment and Spiritual Accompaniment. Pontifical University of Comillas (Spain) 2023.
Master in Pastoral Ministry of Prevention in ecclesial environments of the Pontifical
University of Mexico (Mexico) 2022. Master in Treatment of Learning Difficulties. Central
University of Ecuador (Ecuador) 2013. Higher Diploma in University Learning
Management. Polytechnic School of the Army (Ecuador) 2009. Bachelor's Degree in
Educational Sciences, Educational Psychology and Guidance. Central University of Ecuador
(Ecuador) 2006.
Currently she is a teacher of the Psychopedagogy career at the Central University of
Ecuador.
MARIELA NÚÑEZ-SANTIANA, holds the following degrees: Master's Degree in Teacher
Training in Secondary Education of Ecuador in Geography and History- UNIVERSITAT DE
BARCELONA, (Spain) 2016, Bachelor of Science in Education, mention in Basic Education-
Technical University of Ambato (Ecuador) 2008.
She is currently a teacher at the Ministry of Education.
Statement of Authorship-CRediT
KLÉVER CÁRDENAS-VELASCO: Conceptualization, methodology, validation, formal
analysis, research, data analysis, first draft and final writing and editing.
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JESENIA MOREIRA-BENAVIDES: Related concepts, organization and integration of
collected data, organization and integration of data, supervision, drafting and revision.
CELIA AMORES-PACHECO: Conceptualization, application of instruments, drafting of
conclusions and recommendations.
MARIELA NÚÑEZ-SANTIANA: Application of instruments, tabulation of results, drafting of
conclusions and recommendations.
