Artificial intelligence, digital competence and
personal hobbies: implications for higher education
Inteligencia artificial, competencia
digital y aficiones personales: implicaciones para la educación superior
Dra. Irene López-Secanell.
Contratada Doctora
Acreditada. Florida Universitaria. España
Dra. Esther Gamero-Sandemetrio.
Contratada Doctora
Acreditada. Florida Universitaria. España
Dra. Estefanía López-Requena. Profesora Ayudante Doctora. Universidad de Valencia. España
Received: 2025/01/24 Revised 2025/01/29 Accepted: 2025/05/10 Online First: 2025/05/21 Published: 2025/05/25
How to cite:
López Secanell, I., Gamero Sandemetrio, E., & López Requena, E. (2025). Artificial
intelligence, digital competence and personal hobbies: implications for higher
education. [Inteligencia artificial,
competencia digital y aficiones personales: implicaciones para la educación
superior]. Pixel-Bit. Revista de Medios y Educación, 73, art.9. https://doi.org/10.12795/pixelbit.115117
ABSTRACT
In the current context of educational transformation
driven by Artificial Intelligence (AI), it is relevant to explore how variables
such as digital competence and personal hobbies are related to the use of this
technology in higher education. This study analyzes this relationship in a
sample of 244 participants (74 teachers and 170 students) from various Spanish
universities. A 33-item ad hoc questionnaire was administered, and the data
were analyzed using non-parametric tests. The results show that: i) there are
no significant differences between teachers and students regarding AI-related
hobbies, with non-technological interests predominating; ii) students use AI
tools more frequently, while teachers are more involved in content creation
through these technologies; iii) students in the Primary Education degree
program show higher levels of overall digital competence, especially in
information literacy, content creation, safety, and problem-solving; iv)
significant differences were observed between universities in digital content
creation; v) no significant gender differences were found. These findings
contribute to a better understanding of AI use in higher education from a
competence-based and contextual perspective.
En el actual contexto de transformación educativa impulsada por la
Inteligencia Artificial (IA), resulta relevante explorar cómo variables como la
competencia digital y las aficiones personales se relacionan con el uso de esta
tecnología en el ámbito universitario. Este estudio analiza dicha relación en
una muestra de 244 participantes (74 docentes y 170 estudiantes) procedentes de
distintas universidades españolas. Se aplicó un cuestionario ad hoc de 33
ítems, y los datos fueron analizados mediante pruebas no paramétricas. Los
resultados muestran que: i) no existen diferencias significativas entre
profesorado y alumnado en cuanto a aficiones vinculadas con la IA, predominando
intereses no tecnológicos; ii) los estudiantes utilizan con mayor frecuencia
herramientas de IA, mientras que el profesorado destaca en la creación de
contenidos mediante estas tecnologías; iii) el alumnado del grado de Educación
Primaria presenta mayores niveles de competencia digital global, especialmente
en alfabetización informacional, creación de contenidos, seguridad y resolución
de problemas; iv) se observaron diferencias significativas entre universidades
en creación de contenido digital; v) no se encontraron diferencias
significativas según el sexo. Estos hallazgos permiten avanzar en el
conocimiento sobre el uso de la IA en educación superior desde una perspectiva
competencial y contextual.
.
KEYWORDS· PALABRAS CLAVES
Artificial intelligence; higher education; digital
competence; teachers; educational technology
Inteligencia
artificial; educación superior; competencia digital; docentes; tecnología
educativa
1. Introduction
Artificial Intelligence (AI) is transforming education
by improving teaching-learning processes and automating administrative tasks,
allowing for more personalized experiences. According to Rojas (2015), AI
focuses on developing machines capable of reasoning and solving problems more
efficiently than humans. Its application in education has generated interest in
how it impacts students and teachers (Dawson et al., 2023; Flores-Vivar &
García-Peñalvo, 2023).
Although the use of AI facilitates the creation of
tailored educational content and releives teachers of bureaucratic tasks, it
also poses ethical and pedagogical challenges. The United Nations Educational,
Scientific and Cultural Organization (UNESCO) (2022) highlights the importance
of using AI ethically, ensuring data protection and student privacy. In this
regard, Sanabria-Navarro et al. (2023) emphasise the need for a critical
approach to balance human-machine interaction in teaching.
Despite the growth in research on AI and education
(Valencia & Figueroa, 2023), there is a lack of studies on the relationship
between AI, hobbies and digital competence of teachers and students. To address
this gap, the article examines the AI tools used by university professors and
future teachers at different educational levels, relating them to their
interests and extracurricular activities.
This study is relevant because it allows for an understanding
as to how hobbies influence the adoption of AI, identifying opportunities to
improve digital learning and analyzing the integration of AI in distance
education. With a significant sample from the International University of
Valencia, we explore strategies to adapt AI to virtual environments, favoring
more autonomous and personalized learning processes.
1.1. Approaches to the origins of
AI
The period between 1950-1960 is when initial ideas
about the possibility of creating artificial entities arose. Alan Turing’s contribution during this period is particularly notable:
in a 1950 article published in the journal Mind, he advanced the notion that
machines could think. However, the concept of “Artificial Intelligence”
was first coined in 1955, when John McCarthy proposed the organization of a
workshop dedicated to this topic during the summer of 1956 (McCarthy et al.,
2006).
During the following decade (1960s-1970s), the first
programs capable of emulating human reasoning were developed, which gained
momentum between the 1980s and 1990s. The latter period gave rise to the
emergence of learning approaches inspired by nature, such as artificial neural
networks and genetic algorithms.
Towards the end of the 20th century (1990-2000), the
foundations of AI were consolidated in areas such as computer science and
robotics. It was during this period that AI came into use in the industrial
field. This applicability attracted the attention of companies such as Google,
which began to invest in the development of AI algorithms.
Since 2010, significant advances in this technology
have emerged, making it a fundamental tool across various sectors—from medicine
to education—revolutionizing how we tackle complex problems and creating new
opportunities for innovation (Russell & Norvig, 2010).
1.2 Use of AI in higher
education: opportunities and challenges
The use of Artificial Intelligence (AI) in education
has grown significantly, with studies analyzing its benefits and challenges.
Although a promising future is in sight, there are ethical implications that
require regulation (Flores-Vivar & García-Peñalvo, 2023). Organizations
such as UNESCO (2021) and the European Commission (2020) have sparked debates
on equity in access and the need for an ethical framework based on privacy,
transparency and accountability.
AI in education offers various opportunities, such as
improving teaching globally, expanding access to knowledge, optimizing
operational tasks and increasing efficiency in various professions (Long and
Siemens, 2011). In addition, it facilitates autonomous and personalized
learning through academic monitoring tools and optimization of decision making
in educational institutions.
However, there are also risks such as a widening
digital divide, displacement of workers, marginalization of groups without
technological access, and over-reliance on AI. Ethical concerns about data
misuse and information privacy also arise.
In this context, generative AI stands out for its
ability to create original content, such as texts, images and music (Bonilla et
al., 2024). Models such as ChatGPT can enrich teaching through the programming
of activities, evaluation and personalization of didactic resources
(Flores-Vivar & García-Peñalvo, 2023). In addition, it enables the design
of learning tools such as rubrics, educational trivia and interactive stories,
adapting the content to the individual needs of students (García-Peñalvo et
al.,2024).
Despite its challenges, generative AI represents a key
opportunity to transform education and improve teaching through more innovative
and personalized approaches.
1.3 Digital competence in a
university context
The integration of AI in society has drawn attention to
the educational field, generating a growing interest in the level of digital
competence of future teachers and active teachers.
According to UNESCO (2021), educational systems must
ensure that students are adequately prepared to function in a world where AI
has emerged to transform our way of life. In this context, it is essential that
both students and teachers possess strong digital skills.
The European Framework of Digital Competence for
Educators (DigCompEdu) (Redecker, 2020) defines digital competence as the
ability to use digital technologies, not only to improve teaching, but also in
their professional interactions. This framework argues that digital competence
in education is not limited to the mere use of technologies and thus also
implies considering how they are integrated into the teaching-learning
processes. It is therefore “part of educators' digital competence to enable
students to actively participate in living and working in a digital age” [author’s
translation] (Redecker, 2020, p.17). The following table outlines the specific
competencies required to facilitate the development of digital competence among
the student body (see Table 1).
Table 1
Specific competencies and sub-competencies for the
development of students' digital competency
|
Specific competencies |
Specific sub-competencies |
|
Professional engagement |
Organizational
communication, Professional collaboration, Reflective practice, Continuous
professional development through digital media. |
|
Digital content |
Selection of digital
resources, Creation and modification of digital resources, Protection,
management and exchange of digital content. |
|
Teaching and learning Evaluation and feedback Student empowerment Development of students'
digital competencies |
Teaching, Guidance and
support in learning, collaborative learning, self-regulated learning. Assessment strategies,
learning analytics, feedback, scheduling and decision making. Accessibility and inclusion,
personalization, active engagement of students in their own learning. Information and media
literacy, Digital communication and collaboration, Digital content creation,
Responsible use, Digital problem-solving |
Nota. Redecker
(2020, p.25)
To enrich this competency framework, the TPACK model
provides a deeper understanding of the skills needed by teachers in a digital
environment. This model establishes a conceptual framework for understanding
the knowledge a teacher must have to effectively integrate technology into
teaching. According to this model, three types of key knowledge are identified:
i) technological knowledge (TK), which involves understanding how technology
works; ii) content knowledge (CK), which helps us understand the subject to be
learned or taught; and iii) pedagogical knowledge (PK), which involves training
in teaching-learning strategies and didactics to practice the profession.
The interaction between these three types of knowledge
gives rise to three forms of emergent knowledge, derived from the combination
of two of them: (i) Disciplinary Content-Pedagogical Content (PCK), which denotes
the amalgamation of content and pedagogy without the application of technology;
(ii) Disciplinary Content-Technological Content (TCK), where teachers must have
a deeper understanding of the subject matter and employ technology to some
extent; and (iii) Pedagogical Content-Technological Content (TPK), which highlights
“how“ teaching and
learning can transform pedagogy and teaching method when technologies are
integrated. This fusion involves pedagogical understanding and a strong command
of technological tools. The purpose of the model lies in technological
pedagogical content knowledge (TPACK), which is achieved when all three
elements converge.
Both the DigCompEdu competency framework (Redecker,
2020) and the skills developed in TPACK will not generate a significant impact
on their own. Their effectiveness will lie in how they are integrated into
pedagogical practices and how they will transform teaching and learning
methods. A review and an update of educational strategies is therefore required
to achieve significant changes and take full advantage of the potential of
technology in university education.
In this vein, Revuelta-Domíngez et al. (2022)
highlight a growing interest in developing teachers' digital competence through
training and assessment models, but also stress the need for policy makers to
design comprehensive and continuous training plans that can be directly applied
to teaching practice in the classroom. Authors such as Spirina (2018), Flores-Vivar
and García-Peñalvo (2023) propose that AI and its use should be incorporated
into school and university curricula and, therefore, suggest options such as
working with AI in extracurricular activities such as seminars, workshops or
organizing hackathons.
Based on this evidence, the research developed has
been guided by the research question: To what extent does AI relate to the
hobbies and digital competence of students and teachers? The general objective
that arises from this research question is to analyze the relationship between
AI, hobbies, and the digital competence of students and teachers.
Based on this general objective, the following
specific objectives are proposed:
·
To analyze the relationship between AI and the hobbies
of students and teaching staff in response to the research question: To what
extent are there differences between students and faculty in AI-related
hobbies?
·
To determine whether there are differences between
students and teachers in terms of digital competence and/or use of AI resources
in response to the research question: To what extent does the use of AI and
other digital competencies differ between teachers and students?
·
To determine whether there are gender differences with
regard to digital competence and/or use of AI resources in response to the
research question: To what extent do digital competencies—such as AI use—depend
on gender?
·
To determine whether there are differences according
to educational level in the level of digital competence and/or use of AI
resources in response to the research question: To what extent do digital
competencies—such as AI use—depend on educational level?
·
To determine whether there are school-based
differences related to digital competence and/or use of AI resources in
response to the research question: To what extent do digital competencies—such
as AI use—differ between educational centres?
As an initial working hypothesis, it is proposed that
there are differences between students and teachers in terms of AI-related
hobbies, digital competence and the use of AI resources, with a higher
percentage of affirmative responses from students. No gender differences are
anticipated, while individuals with higher educational levels are expected to
demonstrate stronger digital competencies.
2. Metholology
2.1 Sample
The work sample is made up of 244 participants, 74
teachers and 170 students. Among the teaching staff, 70% are women, 28% are men
and 2% prefer not to answer this question. 50% of the teachers teach in the
Early Childhood Education degree, 5% in the Primary Education degree, 20% in
the dual Early Childhood/Primary Education degree, and 25% in the Master's
program for Secondary Education. As for their origin, 66% of the teachers are
from Florida Universitària, 27% from the International University of Valencia
(VIU) and 7% from the University of Lleida (UDL).
Among the student body, 71% are women, 27% are men,
and 2% prefer not to answer.
16% are enrolled in the Early Childhood Education degree, 35% in the Primary
Education degree, 18% in the dual Early Childhood/Primary Education degree, and
31% in the Master's program for Secondary Education. 87% of the students come
from Florida Universitària, 4% from the International University of Valencia
(VIU), and 9% from the University of Lleida (UDL).
2.2 Data acquisition and
processing
To address the objectives of this research, a
non-experimental design within the positivist paradigm was proposed, defining
the variables to be measured based on two validated questionnaires that were
adapted to the present study and carried out online depending on whether the
sample was the teaching staff or the students. In relation to the questionnaire
on the use of AI resources, the instrument developed within the framework of
the Innovative Centers Project (DIM-EDU Educational Network) proposed by
Marquès (n.d.) was used and adapted. Although this questionnaire was originally
designed for early childhood and primary school teachers, adjustments were
applied to this study so that the language and formulation of some items could
be adapted to both teachers and university students without modifying the
conceptual content. This adaptation was reviewed by two experts in educational
innovation to ensure content validity.
The questionnaire used in the present investigation
consists of the following dependent variables:
1. Relationship of hobbies with AI. It consists of 1
question to which the answer can be “Yes” or “No”. For the analysis, “Yes” is
given a 1 and “No” a 0.
2. A self-diagnosis
questionnaire of digital competencies defined by the Ministry for Digital
Transformation and of the Civil Service within the Generation D Pact program
(Ministry of Economic Affairs and Digital Transformation, n.d.). It consists of
21 questions based on the Model of the Digital Competences of Spanish
Citizenship. The variables it measures are:
a. Information
and data literacy (Question 1, 2, 3). Maximum score: 9.
b. Communication
and collaboration (Question 4, 5, 6, 7, 8, 9). Maximum score: 18.
c. Digital
content creation (Question 10, 11, 12, 13). Maximum score: 12.
d. Security
(Question 14, 15, 16, 17). Maximum score: 12.
e. Problem-solving
(Question 18, 19, 20, 21). Maximum score: 12.
For each question, there are three statements about
digital practices. Each of the statements must be answered by marking the
option “Yes” or “No”. For the analysis, “Yes” is given a 1 and “No” a 0. The
higher the score, the higher the competence in each area.
In the present study, all the variables of the questionnaire,
as well as all the associated questions, have been taken into account.
3. Questionnaire
on the use of AI Resources in teaching and learning processes, designed within
the Innovative Centers Project (DIM-EDU Educational Network) (Marquès, n.d.).
The original questionnaire is addressed to teachers and measures the following
variables:
a. Training
received on AI and its appropriate use for teaching and learning.
b. Use of
AI resources.
c. Student's
use of AI resources.
d. Advantages
that arise when integrating these AI Resources.
e. Problems that are associated
with the integration of these AI Resources.
f. Adaptations
that have been made in the center when integrating the use of the AI Resources.
For each section, different statements about the use
of AI are presented. Each statement must be answered by marking the “Yes” or
“No” option. For the analysis, “Yes” is given a 1 and “No” a 0. The present
work has taken these three areas into account:
a. Use of AI
resources (7 statements, maximum score 7)
b. Advantages
and problems observed when integrating these AI resources (5 statements,
maximum score 5)
c. Use of AI
resources in the classroom (3 statements, maximum score 3)
The dependent variables in this study therefore are:
1. The relationship
between hobbies and AI
2. Digital
competence. Subvariables:
a. Information
and data literacy
b.
Communication and collaboration
c. Digital
content creation
d. Security
e. Problem-solving
3. Use of AI
resources in the teaching-learning process. Subvariables:
a. Use of AI
resources
b. Advantages
and problems observed when integrating these AI resources
c. Use of AI
resources in the classroom
The independent variables of the study are as follows:
1. Sample: two
established groups:
1. Teachers
2. Students
2. Gender: three
established groups:
0. Male
1. Female
2. Prefer not to answer.
3. Educational
level: four established groups:
0. Early Childhood Education
1. Primary Education
2. Dual Early Childhood/Primary education
3. Master's in Secondary Teaching
4. Center:
three established groups:
0. Florida Universitària
1. International University of Valencia (VIU)
2. University of Lleida (UDL).
The data collection process was carried out during the
first quarter of the 2023-2024 academic year. At campus-based universities, one
teacher was selected from each degree program to facilitate access to the questionnaire
during face-to-face classes. In the case of the online university, the questionnaire
was conducted live during a synchronous session by the teaching staff.
Participation was voluntary and anonymous, and all participants were informed
about the objectives of the study and the intended use of the data, thereby ensuring
informed consent.
2.3. Statistical analysis
To analytically verify the normal distribution of the
data, the Kolmogorov-Smirnov test and the Shapiro-Wilk test were used. In all
numerical results showing a statistical analysis of the data, statistical
significance was obtained using the non-parametric Mann-Whitney U test (for 2
samples) and the Kruskal-Wallis test (for k-samples) with a two-tailed
significance level in order to analyze whether there are differences in the
values of the dependent (quantitative) variables between the groups defined by
the independent (categorical) variables. Values with a p-value less than or
equal to 0.1, corresponding to a 90% confidence interval, were considered
significant. Statistical analyses were performed using the statistical analysis
program SPSS (Statistical Package for Social Sciences, version 24).
2.4. Ethical considerations
To analytically verify the normal distribution of the
data, the Kolmogorov-Smirnov test and the Shapiro-Wilk test were used. In all
numerical results showing a statistical analysis of the data, statistical
significance was obtained using the non-parametric Mann-Whitney U test (for 2
samples) and the Kruskal-Wallis test (for k samples) with a two-tailed
significance level in order to analyze whether there are differences in the
values of the dependent (quantitative) variables between the groups defined by
the independent (categorical) variables. Values with a p-value less than or
equal to 0.1, corresponding to a 90% confidence interval, were considered
significant. Statistical analyses were performed using the statistical analysis
program SPSS (Statistical Package for the Social Sciences, version 24).
3. Analysis and results
Initially, to determine whether the behavior of the
dependent variables (and subvariables) was normal or not, Kolmogorov-Smirnov
and Shapiro-Wilk normality tests were performed.
In both tests, the coefficient is p < .05, so the
null hypothesis is not accepted, and the sample does not behave normally (Table
2).
Table 2
Normality tests on dependent variable data
Note. Df =Degrees of freedom, p =Signification or p-value.
Based on the normality results, the non-parametric
Mann-Whitney U test (2 samples) and Kruskal-Wallis test (k-samples) were used.
In response to the question, “Are there differences
between students and teachers in terms of AI-related hobbies?”, no significant
differences were observed between teachers and students in terms of AI-related
hobbies. In fact, hobbies unrelated to AI, such as sports, drawing, reading,
and traveling, prevail in both groups (Figure 1).
Figure 1
Histograms of AI-related hobbies among students and
teachers
In relation to the research question, “Do digital competencies
such as the use of AI differ between teachers and students?” (objective 2), the
results show that there are no significant differences in the dependent
variables (the level of digital competence and the use of AI resources between
students and teachers). In terms of the subvariables, significant differences
(p-value = .029) were observed between the two groups in the creation of
digital content, with a higher level of creation among teachers and in the use
of AI resources (p-value = .095), with higher use among students (Figure 2).
Figure 2
Mann-Whitney U test for dependent variables (1)
“digital content creation” (2) “use of AI resources” based on the sample
(teachers/students)
Regarding the next research question, “Do digital competencies
such as the use of AI depend on educational level?” (objective 4), significant
differences were identified with regard to the overall level of digital
competence (p-value = .069), which was higher in primary education (Figure 3).
Within the subvariables that define digital competence, considerable
differences were observed in: Information and data literacy (p-value = .07),
Digital content creation (p-value = .044), Security (p-value = .085), Problem-solving
(p-value = .009). In all cases, the level of competence was higher in primary
education. No differences were observed according to educational level in the
dependent variables: AI-related hobbies and use of AI resources.
Figura 3
Kruskal-Wallis test for the dependent variable “level
of digital competence” according to educational level (Early childhood Education,
Primary Education, Dual Early Childhood/Primary Education, Master’s in Secondary
Teaching)
On the other hand, the question as to whether digital competencies
such as the use of AI differ between educational institutions (objective 5) is
raised. Significant differences were observed in the use of AI resources
(p-value = 0.077), with greater use at the University of Lleida (UDL) (Figure
4). Within the subvariables, it was observed that teachers at the University of
Lleida make greater use of AI resources (p-value = 0.023) and have better
digital problem-solving skills (p-value = 0.076). In addition, the University
of Lleida and the International University of Valencia show greater competence
in digital content creation (p-value = 0.016).
Figura 4
Kruskal-Wallis test for the dependent variable “use of
AI resources” according to the center (Florida Universitària, the International
University of Valencia, the University of Lleida)
Regarding the last research question, “Do digital
comptencies such as the use of AI depend on gender?” (objective 3), no significant
differences were observed in any variable or subvariable.
4. Discussion
The results obtained facilitate a more in-depth
analysis of the links between AI, personal hobbies, and digital competence within
higher education contexts. Some of the most notable findings in relation to the
theoretical framework developed above are discussed below.
Firstly, no significant differences were observed
between teachers and students in terms of hobbies related to AI, with an even
higher prevalence of hobbies unrelated to AI. This indicates that those surveyed
still exhibit reluctance to use technology during their leisure time.
Therefore, in line with Spirina (2018) and Flores-Vivar and García-Peñalvo
(2023), AI and its use should be incorporated into school and university
curricula. Thus, these authors propose options such as working with AI in
extracurricular activities such as seminars, workshops, or hackathons. In this
way, we would see an increase in the use of AI in our daily lives in the long
term, both in leisure and professional settings. This low level of engagement
could be related to several factors, such as the perception of AI as a strictly
academic or professional tool, the lack of cultural references that integrate
technology into leisure, or even a certain generational resistance to the use
of these tools outside the workplace. The limited integration of AI in informal
settings could be hindering its critical appropriation by students and
teachers, which has important implications for the development of comprehensive
digital competencies. Therefore, promoting AI-mediated recreational or leisure
experiences could be key to increasing familiarity with and acceptance of this
technology in all areas of life.
Secondly, there is evidence of a differentiated use of
AI according to academic role: while students use AI tools more frequently to
support their learning processes, teachers make greater use of these tools for
content creation. This result is consistent with the approach of the DigCompEdu
Framework (Redecker, 2020), which emphasizes the need for teachers to integrate
AI to enrich their teaching practices. It is also in line with the
contributions of Bonilla et al. (2024), who highlight the potential of
generative AI to improve the planning and development of adaptive and
innovative educational materials. Furthermore, as pointed out by Chiappe, San
Miguel, and Sáez (2025), the emergence of generative AI raises questions about
the traditional role of teachers and the need to redefine their function in the
educational process, as this technology could complement and significantly
transform teaching practices. This differentiated use can be explained, in
part, by the different purposes for which students and teachers approach AI:
while students use it to efficiently complete specific tasks, teachers adopt a
more technical and creative approach, incorporating AI into lesson planning.
This reflects a possible functional gap in the use of technology, which could
be addressed with specific training tailored to the needs of each group. In
this case, if a more cross-functional and shared use of AI is not promoted
between teachers and students, there is a risk of reproducing a pedagogical
model in which teachers continue to assume a centralizing role, rather than
facilitating more distributed and collaborative learning.
Thirdly, in terms of overall AI competence,
significant differences can be observed, with primary education showing the
highest percentage in information and data literacy, digital content creation,
security, and problem-solving. As pointed out by UNESCO (2021), school systems
must ensure that students are adequately prepared to function in a world where
AI has arrived to transform our ways of life. Therefore, students and teachers
must reflect on the responsible and ethical use of AI, increasingly incorporating
strategies into their daily lives that allow them to observe all the
possibilities it offers, such as expanding access to a wide variety of
knowledge, facilitating the execution of operational tasks in various fields,
and even increasing efficiency in different professions (Vera-Rubio et al.,
2023; Selwyn et al., 2022; Zárate, 2021). Although the prospects are
encouraging, they entail important ethical and deontological considerations
that necessitate appropriate legislative regulation. It is important to
recognize that, within the current pedagogical paradigm, students occupy a
central role in the learning process. In this context, tools such as AI can
facilitate greater emphasis on project-based learning, flexible learning,
collaborative learning, and self-regulated learning, thereby enhancing the
overall quality of education and contributing to the advancement of educational
systems (Flores-Vivar & García-Peñalvo, 2023). In this sense, the
incorporation of AI as an educational resource during initial teacher training
may be key to developing these skills (Ayuso del Puerto & Gutiérrez, 2022).
Initial teacher training programs should review and update their training
strategies to ensure equitable technological literacy across different
educational stages. These results also emphasize the need to adapt practice and
assessment environments to ensure that digital competence is not only developed
but also consolidated and integrated into professional practice.
Finally, other interesting results from this study are
linked to the lack of gender differentiation in the results for the different
variables analyzed. Thus, it can be observed that the greatest differentiation
lies in the lack of training in this area and the lack of awareness of many of
the possibilities that AI offers in educational environments. This may explain
why some educational environments and/or settings are more conducive than
others to its use, given that the University of Lleida (UDL) is the institution
that uses AI the most according to the sample in this study. In addition, both
the University of Lleida and the International University of Valencia show
greater competence in the creation of digital content. In this case, both are
university settings; therefore, as highlighted in the recent report by Pedreño
et al. (2024), the role of AI in this sphere is pivotal for the renewal and
transformation of universities. Nevertheless, within the European context, the
outcomes related to the integration of AI in these environments, the training
of teaching staff regarding the opportunities it presents in the classroom, as
well as the development of policies governing its appropriate use, remain
comparatively limited. The absence of gender differences could be interpreted
as a positive sign of democratization of access to digital competencies ;
however, it may also be masking more subtle inequalities linked to the type of
training received, perceived self-efficacy, or access to practical experience
with AI. As for the differences between schools, these could be related to
institutional factors, such as strategic commitment to innovation, the
existence of clear digitization policies, or the presence of pedagogical
leaders who promote the use of AI. This implies that the institutional context
plays a key role in technology adoption, reinforcing the need to implement
consistent and sustainable policies that ensure adequate training at all levels
and in all regions.
5. Conclusions
The findings of this study underscore the growing
importance of artificial intelligence (AI) in education, as well as the need to
continue exploring its impact on the digital competence of teachers and
students. Although no significant differences were identified in AI-related
hobbies between the two groups, the results reflect greater use of AI tools by
students and greater use for content creation by teachers. It was also observed
that digital competence varies according to educational level, with primary
education standing out as the group with the highest levels of information
literacy, digital content creation, security, and problem- solving.
On a practical level, the results of this research not
only provide empirical knowledge about the use and perception of AI in the
university context, but also offer a solid basis for pedagogical and
institutional decision-making. First, they help guide the design of digital
competence and AI training programs tailored to the different needs of teachers
and students. The results can also be used to select the most appropriate AI
tools based on user profiles (content creation, automation, or learning
personalization).
The data obtained in the study underscore the
importance of continuous training and the development of pedagogical strategies
that integrate AI in an ethical and effective manner. Furthermore, the
difference in the use of AI among educational institutions suggests the need
for uniform policies that promote equity in the implementation of these
technologies. In this sense, this study provides empirical evidence on the
interrelationship between AI and education, providing a starting point for
future research that delves deeper into the challenges and opportunities posed
by its incorporation into a higher education context.
5.1. Limitations and future lines of research
This study presents certain limitations that warrant
consideration. In particular, the exclusive use of quantitative methods
constrains our understanding of individual motivations and perceptions;
therefore, future research should incorporate qualitative approaches. Also,
setting significance at p ≤ 0.1 reflects the exploratory nature of the
work, but suggests the need to replicate these analyses with more rigorous
statistical criteria. Finally, although the originality of the approach is
noteworthy, it would be useful to explore how these results fit into existing
conceptual frameworks on digital competence and technology adoption in greater
depth, thereby enabling a more theoretical and contextualized contribution to
the field.
Author contributions
Conceptualization, M. Esther Del-Moral-Pérez, Nerea
López-Bouzas; data curation, Jonathan Castañeda-Fernández; formal analysis, M.
Esther Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan Castañeda-Fernández;
funding acquisition, M. Esther Del-Moral-Pérez; investigation, M. Esther
Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan Castañeda-Fernández;
methodology, M. Esther Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan
Castañeda-Fernández; project administration, M. Esther Del-Moral-Pérez;
resources, Nerea López-Bouzas; software, Nerea López-Bouzas; supervision, M.
Esther Del-Moral-Pérez; validation, Jonathan Castañeda-Fernández;
visualization, Nerea López-Bouzas; writing—original draft preparation, M.
Esther Del-Moral-Pérez, Nerea López-Bouzas, and Jonathan Castañeda-Fernández;
writing—review and editing, M. Esther Del-Moral-Pérez, Nerea López-Bouzas, and
Jonathan Castañeda-Fernández
Funding
This research has not received
external funding
Data Availability Statement
The
data set used in this study is available at reasonable request to the
corresponding author
Ethics approval
Not aplicable
Consent for publication
The author has consented to
the publication of the results obtained by means of the corresponding consent
forms.
Conflicts of interest
The author declares that they
have no conflict of interest
Rights and permissions
Open Access. This
article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation,
distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons licence, and indicate if changes were made.
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