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Article |
Generando contenido en la educación digital a través
de herramientas de Inteligencia Artificial. Una Revisión Bibliométrica
Seis Mendoza, Luis Amando[*]
Abstract
The development of educational content using artificial intelligence
algorithms has been implemented favorably to increase dynamics within and in
autonomous activities as a methodological tool to improve cognitive learning
qualities in students. Digital educational models in the wake of COVID-19 have
accelerated scientific research on the use of artificial intelligence to
support student academic performance, focusing primarily on computer-assisted
learning dynamics. This study uses bibliometric analysis to evaluate the
performance of these predictive mathematical algorithms on scientific learning
over the last 50 years, demonstrating that this self-learning gap has generated
indispensable content for education today. An introduction to artificial
intelligence models as a tool for assisted learning is presented. The
implications of digital education and the resources that can be used in
learning dynamics and didactics are analyzed, generating variables that define
their degree of use and level of learning through specialized review literature
using the Prisma2020 tool.
Keywords: educational model, digital education, predictive algorithms
Resumen
El desarrollo de contenido
educativo mediante algoritmos de inteligencia artificial se ha implementado de
forma favorable para aumentar la dinámica dentro y en actividades autónomas
como herramienta metodológica para mejorar las cualidades de aprendizaje
cognitivo en estudiantes. Los modelos educativos digitales a raíz de la
COVID-19 han acelerado la investigación científica sobre el uso de la
inteligencia artificial para apoyar el rendimiento académico estudiantil,
centrándose principalmente en dinámicas de aprendizaje asistido por
computadora. Este estudio mediante un análisis bibliométrico evalúa el
desempeño de estos algoritmos matemáticos predictivos sobre el aprendizaje
científico a través de los últimos 50 años demostrando que esta brecha de
autoaprendizaje ha generado contenido indispensable para la educación en la
actualidad. Se presenta una introducción a los modelos de inteligencia
artificial como herramienta para el aprendizaje asistido. Se analizan las implicaciones
de la educación digital y los recursos que se pueden emplear en la dinámica y
didáctica de aprendizaje, generando variables que definen su grado de uso y
nivel de aprendizaje a través de literatura de revisión especializada empleando
la herramienta Prisma2020.
Palabras clave: modelo educativo, educación digital, algoritmos predictivos
Introduction
The development of online education before, during,
and after COVID-19 represented a major challenge for many countries around the
world, as it presented conditions for which developing countries and some
regions of Latin America were unprepared, both in terms of infrastructure and
pedagogy. This model of online education was implemented unevenly, depending on
institutional policies, study dynamics, adaptations by educational centers, and
psychosocial factors. These elements contribute greatly to inequality in
education.
Developing countries lack a viable educational model that meets the
highest standards of the global market, but this should not undermine the
efforts of educators to strengthen their educational systems. These educational
barriers have caused some countries already mired in poverty to fall further
behind in their development, while others have found opportunities in digital,
technological, and smart production, as well as in the media, which has given
them international visibility, as in the cases of Singapore and Iceland. These
strategies were based on a vision of the future of global needs and the
emergence of new buyers in the global context and the fourth industrial
revolution.
After the COVID-19 pandemic, the use of digital platforms to access basic
services, including education, health, food, and work, grew significantly. This
led to the adoption of policies focused on virtualizing educational processes
and strengthening digital classrooms. Authors such as Vivas and Passiani assert
that, although e-learning educational methodologies have driven the development
of education in virtual classrooms, the dynamics remain deficient because they
do not propose control of the type of artificial intelligence tools or the
ability to impart knowledge without it being seen as part of a logical AI
process, which means that users do not generate real learning in the medium or
long term.
In Ecuador, digital education has become known since the COVID-19
pandemic as a tool to replace face-to-face learning, but it has been hampered
by technological infrastructure limitations and the ability of educators to
adapt and complement traditional methods of education outside the classroom.
Recent literature highlights the results and implications of how
complexity and contingency affect teaching and learning. Based on its learning
potential, the digital education model can be characterized as having been
consolidated through an AI-assisted methodology decoded into various algorithms
that improve user preferences for use and learning.
Thanks to these preferences, spaces for digital content have been
designed that improve the capacity of the AI tool as an educational assistant
that stimulates learning through teaching methods and platforms that enhance
the user experience and retention. In this sense, digital education has opened
the door to active education during COVID-19, which has given rise to virtual
classrooms or distance or programmed learning spaces, which have taken on
various forms, such as synchronous and asynchronous, bringing students much
closer to digital education.
In a recent analysis of the specialized literature, several studies have
pointed out unfavorable effects linked to the use of artificial intelligence in
educational environments. According to the systematic review, around 20% of
students report a decrease in their cognitive autonomy due to their growing
dependence on automated tools. Likewise, the authors report that approximately
18% of teachers identify difficulties in maintaining the balanced development
of critical and creative skills when AI models are integrated into training
activities. The research also shows that about 16% of educational institutions
have faced risks associated with privacy and data management, which represents
one of the main ethical challenges in the adoption of these technologies. These
percentages reveal that, while AI has driven advances in digital learning, it
also generates significant tensions that need to be considered in its
pedagogical implementation.
In recent years, various studies have highlighted limitations and adverse
effects associated with the integration of artificial intelligence into
teaching methodologies. Zhu points out that excessive use of AI-based
conversational systems can reduce students' critical thinking and cognitive
autonomy, noting that in a significant percentage of the studies analyzed,
students accept between 60% and 70% of AI responses without verification, which
can lead to conceptual errors and intellectual dependence.
Similarly, Urzúa's review identifies that 48% of the articles examined
report ethical or pedagogical challenges arising from the use of language
models, especially in automation-centered methodologies. Among these problems
are a lack of transparency, reduced teacher-student interaction, and poor
pedagogical supervision in AI-assisted digital environments.
At the methodological level, González-Espinosa found that only 32% of
AI-based teaching proposals fully comply with human-centered design criteria,
which implies that a considerable majority (68%) have shortcomings related to
reliability, usability, ethics, and teacher participation in the instructional
process. This data is relevant, as it shows that a large part of AI-supported
methodologies are not developed with the student's comprehensive pedagogical
experience as a priority.
Finally, Schmidpeter & Funk, in an empirical study at the university
level, report that 41% of students express concern about the reliability of
intelligent systems in academic activities, and 37% acknowledge having
experienced a decline in their ability to solve problems without AI assistance,
suggesting a negative impact on essential learning processes when the
methodology relies excessively on these tools.
Taken together, these findings lead to the conclusion that, while
AI-assisted teaching methodologies offer significant opportunities for
educational innovation, the most recent studies agree that there are
unfavorable percentages that reveal cognitive, ethical, and pedagogical risks.
These data underscore the need to strengthen methodological approaches, promote
critical and balanced use of AI, and ensure teacher integration in the design
of learning experiences mediated by intelligent technologies.
According to the UN report on education, 5.35 billion people, or 66.2% of
the global population, use the internet worldwide. The number of internet users
increased by 97 million people, representing a 1.8% increase compared to 2023.
Based on this data, research has shown that access to educational society is
around 70 to 72%, which is considered moderately acceptable.
Analysis of this report shows that the digital education model still has
a 28% educational accessibility gap worldwide, which does not create equal
opportunities for all students. However, the asynchronous modality has made it
possible to schedule and record classes that tend to be shared, thus increasing
accessibility to education.
Given the apparent success of the digital education model, artificial
intelligence has established itself as a tool to support student knowledge
development over the last decade and is currently considered a successful
invention or a boom in contemporary society.
In light of this digital development, this study was conducted using a
bibliometric analysis that evaluates the performance of these predictive
mathematical algorithms on scientific learning over the last decade, comparing
5 years pre-COVID and 5 years post-COVID, demonstrating that this self-learning
gap has generated indispensable content in digital education through a
literature review on scientific platforms such as Scopus, Science Direct, and
Scielo, using the Prisma2020 tool for information synthesis.
A common problem has been identified in various
higher education institutions linked to design courses: many architecture
students find it difficult to develop creative and efficient proposals during
the design process. Although teachers show willingness and effort to address
these difficulties, daily practice reveals several weaknesses that hinder
learning and limit students' ability to innovate, generating a marked tendency
to reproduce existing models. Among the factors that influence this are the lack
of pedagogical and psychological preparation of teachers, a limited ability to
identify and work with different learning styles, and a weak articulation
between the ways of teaching and the ways in which students learn.
Artificial intelligence (AI) is playing an
increasingly important role in teaching methods in digital education, which has
been undergoing significant development over the last decade. The potential of
AI-assisted tools has an evolutionary potential that offers efficiency in the
understanding and delivery of knowledge. One of its most notable applications
is generative design, which proposes increasing dynamics of knowledge,
broadening the spectrum of knowledge to logical processes that increase the difficulty
of knowledge, thus formalizing the so-called digital dynamics.
The capacity offered by a generative tool goes
beyond a simple process; rather, it executes commands that can be adapted to
the user's understanding and meet their specific demands. In students, this
understanding is provided by the teacher or educator who configures the
student's learning levels so that a methodology of logical processes is
executed that corresponds to a correct approach to the virtual classroom where
the dynamics of the class unfold.
The process becomes didactic when it contemplates
active attention to a process that must reach a goal, and as this must be
carried out in a digital environment, it is of great interest to understand
that all digital teaching can have its limitations, since the coding of
processes can evolve to a certain level where it must be adapted and improved
to the next study goal.
Materials and methods
The research is carried out through a systematic
review of high-impact articles in the Scopus and ScienceDirect scientific
databases, structuring an interrelational graph of
the gaps and positive influence of artificial intelligence on the digital
educational model. This data was systematized in AnalizeSearch and Analytics to
obtain the annual impact of publications and determine the indicators that
contribute to the development of spatial learning in student performance. The
hermeneutic method was used, in which a 50-year regression of the development
of digital education and the contribution of artificial intelligence to content
creation as a dynamic and didactic learning methodology was performed. To this
end, texts by representative authors in the global context were selected, along
with their impact, type of contribution, and relevant contribution by authors.
This data was relevant for analyzing their citation index and impact,
generating a correlation of variables such as digital education and artificial
intelligence as a learning methodology. Two phases of bibliometric analysis of
the information were carried out on the PRISMA 2020 and VOSViewer platforms to
generate a checklist and impact graphs for the abstraction of relevant results
from the application of the generative artificial intelligence model in spatial
learning focused on the performance of students at the higher education level.
Results
To develop the analysis, a hermeneutic approach was
applied that allowed for a historical reconstruction of the evolution of the
topic within academic literature. Based on this retrospective examination, it
was identified that scientific interest had already been present since the
mid-1970s. This historical overview has given rise to five fundamental
contributions that have guided and strengthened research in the area. Thanks to
these advances, scientific production has experienced sustained growth, currently
accumulating a considerable volume of publications dedicated to the study of
this topic.
Figure 1. Representation of the bibliometric
systematization of scientific documents on AI.
Source: Own elaboration
Based on a bibliometric analysis of data synthesized
in the Scopus and ScienceDirect scientific databases, it has been possible to
generate a regression of the impact of artificial intelligence and its
influence on digital education, leading to a minimum of 300 publications up to
the year 2000, when the initial terms were linked to emerging computing and
process robotics. From the 21st century onwards, this reality changed with the
introduction of so-called virtual classrooms and generative processes, with a
surge up to 2015 with more than 1,000 publications. The evolution of artificial
intelligence in the last decade has accounted for more than 89% of scientific
publications, already consolidating processes and dynamics of study assisted by
artificial intelligence.
Figure 2. Record of publications on artificial
intelligence classified by author
Source: Own elaboration
In our search for authors in publications, we found
Conati C. to be the most relevant author in generating studies on how
artificial intelligence can be the tool of the future for generating generative
knowledge that replaces human processes and can strengthen confidence in the
results of artificial intelligence through controlled means. His research
highlights the relationship between artificial intelligence and virtual
education, emphasizing how this link benefits the development of critical
thinking in students.
Figure 3. Distribution of publications on AI by country
of origin
Source: Own elaboration
China is currently the leader in publications
covering artificial intelligence tools and how robotics can increase the level
of understanding within so-called virtual classrooms; the dynamics in
conjunction with robotics present students with an evolution towards this new
branch of knowledge. In most of the countries, researchers analyze how the
ethical sphere, the generation of knowledge, and the drive for innovation can
create a dependency that must be combated through control and logical
processes.
Figure 4. Distribution of publications on AI by academic
area
Source: Own elaboration
The literature review found regular and exceptional
patterns linked to the use of artificial intelligence in the education sector.
The results indicate that computer science continues to be the leading area in
the development and implementation of AI in university education. However, as
an increasingly common, albeit unusual, behavior, the social sciences have
begun to integrate these technologies more actively, demonstrating that even
the search for general information or basic knowledge is increasingly being
influenced by the use of AI.
Figure 5. Classification of types of scientific
documents related to AI
Source: Own elaboration
The final findings of the literature review indicate
that more than 3,000 documents are grouped into articles and conference papers,
while the cumulative output up to 2025 exceeds 4,000 works. This increase shows
rapid growth since 2020, when research in artificial intelligence grew by more
than 60%. The analysis revealed that two expressions frequently appeared in
searches conducted on ScienceDirect and Scopus: the relationship between
artificial intelligence and robotics, as well as the appropriate use of
artificial intelligence. These ideas mark the most prominent research trends.
Research that directly responded to variables in the
field of education was selected to analyze the effect of artificial
intelligence on the digital education model. Following this criterion, a
categorization was made based on a comparison between the SJR and Scopus
databases, taking into account indicators such as the number of citations and
impact factors.
The article “Digital game-based learning: Towards an
experiential gaming model” is one of the most cited in this list, with more
than a thousand academic citations. This is considered an atypical case because
its perspective focuses on education through video games as an experimental
alternative to the conventional educational model.
Likewise, the article “Machine learning: Trends,
perspectives, and prospects” stands out for having a very high number of
citations, exceeding several thousand. This case is considered an example of
how machine learning research can be implemented from platforms that improve
continuous learning with assisted education tools for all types of education
and how these processes are becoming an emerging methodology for digital
education.
In conclusion, the future of digital education is
influenced by the operation of artificial intelligence tools and how they
evolve with human knowledge to become more interpretive and humanizing, so
their adoption within education provides facilities and innovation to generate
new content that translates into scientific knowledge.
a. PRISMA 2020 Analysis
The PRISMA 2020 methodology was used, including its
checklists and corresponding flowchart, to systematize and obtain final results
from the scientific article review process. The PRISMA 2020 statement was used
(including the lists of indicators and variables within the search flow), which
included all review articles from the complete search list comprising 9,324
items, some of which included subtopics corresponding to the study indicators
defined by the keywords “digital education model” and “artificial intelligence.”
Structured abstracts of systematic reviews presented in journals and
conferences were included. This yielded the following results:
Table 1. PRISMA 2020 analysis applied to the AI study
b. VOSViewer analysis
In the second stage, a bibliometric analysis was
performed to examine research trends at the intersection of AI, digital
education, and teaching tools during the period 1975-2025. To this end, the
Scopus and ScienceDirect databases were used, selected for their broad scope
and recognition in the academic community. Specific keywords related to AI were
defined to retrieve relevant documents, so that the search was divided into two
concurrent fields that allowed for better visualization of knowledge and learning
of the generative artificial intelligence model. For this purpose, a sequence
data table was structured for the process (see Table 2).
Table 2. Search strategy
|
Tabla
1.
Estrategia de búsqueda |
|
|
AI AND knowledge |
AI AND impact |
|
( TITLE-ABS-KEY ( artificial AND intelligence ) AND TITLE-ABS-KEY ( digital
education model AND teaching tools) ) AND PUBYEAR > 2015 |
(TITLE-ABS-KEY (artificial AND intelligence ) AND TITLE-ABS-KEY
( good use AND impact ) ) AND PUBYEAR > 2015 |
AI AND knowledge
AI AND impact
( TITLE-ABS-KEY ( artificial AND intelligence ) AND
TITLE-ABS-KEY ( digital education model AND teaching tools) ) AND PUBYEAR >
2015
(TITLE-ABS-KEY (artificial AND intelligence ) AND
TITLE-ABS-KEY ( good use AND impact ) ) AND PUBYEAR > 2015Source: Own
elaborationGraphic gamification was handled as a chain of interrelationships
around scientific searches and two groups of variables, namely artificial
intelligence in knowledge and artificial intelligence and its impact.Figure 6.
VOSViewer analysis of AISource: Own elaborationc. Criticism of the supplanting
of artificial intelligence in scientific knowledgeThis criticism highlights that
the proper use of AI can improve learning and research techniques; however, its
misuse can lead to total dependence on and stagnation of scientific knowledge.
An important look at digital educational processes using tools that facilitate
the abstraction of knowledge is both questionable and approved, which is a
paradigm between the proper use and impact of artificial intelligence on
contemporary scientific knowledge in learning.The concept of artificial
intelligence should not stray from the fact that it is a reality and creation
of humanity, which must remain under its control and should not be unbalanced,
as it cannot be totally autonomous. However, thanks to this transformation and
evolution, it has proven capable of making its own decisions that, in the long
run, will not be unethical or dehumanizing, which would affect the perception
of good and evil in terms of an axiom.From this important point of action,
hermeneutics, as the art of interpretation, is the key to knowing how to
understand and learn from artificial intelligence so that the techniques of its
use and the methodology of its operation are understood and interpreted
appropriately without hindering scientific knowledge and the autonomous
advancement of educational societies. It is of paramount importance to maintain
innovation and good research practices in order to generate agents of change
who are not dependent.Therefore, the following critique will interrelate its
proper use from a practical standpoint and how it has evolved within the scientific
field in research that has provided clues that artificial intelligence may at
some point supplant learning and knowledge guided by basic thought dynamics.
The limitations of artificial intelligence make the model highly dependent on
quality data translated into performance variables, so a composite algorithm
can generate a definition of the reality of the design required and a specific
choice of urban context, for which AI algorithms are only as good as the data
they receive. Incorrect or incomplete data can lead to erroneous results.As a
major concern generated by AI, the ethics and systemic evaluation of knowledge
is questionable since the elements of identification, even in the Latin
American educational system and in influence with the case study, do not allow
us to perceive the limits of the application of artificial intelligence and to
what extent it is known that the student has generated new knowledge or is
emulated by AI or assisted robotics.In summary, while AI offers interesting
possibilities for improving education, it also raises serious ethical, social,
and practical concerns. Addressing these criticisms involves finding a balance
between leveraging technology to improve education while ensuring equity,
privacy, and preserving the human elements of teaching.
Over the last 15 years, research on AI and education
has grown significantly, with thousands of publications indexed in databases
such as Web of Science and Scopus. A bibliometric analysis detected more than
5,945 articles published between 1975 and 2025, focusing on topics such as
adaptive learning, applied machine learning, intelligent tutors, educational
robotics, learning analytics, and neural networks applied to the educational
context.
The results confirm that the use of artificial intelligence generates
great interest among the scientific community in citations of scientific
contributions to digital education models and their tools, with 1,078
citations. This attributes the field of technological innovation and social
sciences as generating an important contribution to this type of literature
review. The contributions of artificial intelligence to digital education
models have been around since 1975, which means that it is not a new science,
but rather one that is constantly being reshaped. A look at how it evolves and
develops has generated particular interest, as in just 10 years of literature
review, it has generated more than 80% of contributions at a general level,
with scientific articles on the operation and execution of educational models
using artificial intelligence technologies as the means of dissemination.
Through the PRISMA 2020 analysis, it was found that, with respect to the
27 variables entered for data processing, contributions in artificial
intelligence propose an upward trend in this new year 2025, which may remain
stable and generate new relevant contributions to assisted robotics and the
ability to adopt artificial intelligence from the perspective of advanced
science. This could be a new teaching model, determining the correct
methodology, use, and estimation of the scientific knowledge generated to
promote the deduction of content.
However, the dependence on and lack of deductive interest in the
knowledge being generated could lead to a limitation of critical thinking and
autonomy among students, causing a slowdown in the knowledge curve and thus
expanding the dominance of artificial intelligence over general knowledge and
not promoting a more specialized cognitive development in generating critical
professionals. Therefore, the preservation of knowledge must have limitations
on the use of generative artificial intelligence, establishing rules of use and
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