|
Article |
Deyanira Elizabeth Paz Rubio[*]
Segundo Ignacio Ponte Valverde*
Antenor Oraldo Chávez
Dávila*
Hercules Alexander Guanilo Noriega*
Abstract
Artificial intelligence (AI) has transformed higher
education, optimizing teaching and assessment. This study analyzes the
relationship between the use of AI and teachers' perception of its impact on
education. A quantitative and correlational approach was used with a sample of
100 university teachers. A structured questionnaire was used to assess the
frequency of use and attitude towards AI. The results indicate a significant
positive correlation between the two variables. It is concluded that
familiarity with AI influences its acceptance, although ethical and pedagogical
challenges persist.
Keywords: artificial intelligence, higher education, teacher
perception, automated learning, educational assessment.
Resumen
La inteligencia artificial
(IA) ha transformado la educación superior, optimizando la enseñanza y la
evaluación. Este estudio analiza la relación entre el uso de IA y la percepción
docente sobre su impacto en la educación. Se empleó un enfoque cuantitativo y
correlacional con una muestra de 100 docentes universitarios. Mediante un
cuestionario estructurado, se evaluó la frecuencia de uso y la actitud hacia la
IA. Los resultados indican una correlación positiva significativa entre ambas
variables. Se concluye que la familiaridad con la IA influye en su aceptación,
aunque persisten desafíos éticos y pedagógicos.
Palabras clave: inteligencia artificial, educación superior, percepción docente,
aprendizaje automatizado, evaluación educativa.
Introduction
Higher education is at a turning point due to the
irruption of artificial intelligence (AI). While digitization had already
significantly transformed teaching and learning methods, the accelerated
development of AI has elevated this transformation to an unprecedented level
(Holmes et al., 2021). Tools such as virtual assistants, intelligent tutoring
systems, and personalization algorithms are redefining how students interact
with knowledge and how teachers design their pedagogical strategies (Cardona et
al., 2023). The integration of AI in higher education has made it possible to
optimize administrative and academic processes, automate assessments, and
provide more personalized learning experiences (UNESCO, 2023). However, its
implementation has generated debate among its promoters and critics, because
while it offers opportunities to improve teaching efficiency, it also poses
challenges related to the dehumanization of learning, equity in access to
technology, and teacher training in digital competencies (Zawacki-Richter et
al., 2019).
Since its incorporation in education, AI has
demonstrated its ability to improve administrative efficiency, optimize student
performance assessment, and deliver personalized learning experiences (UNESCO,
2023). Models from machine learning can process large volumes of data and
detect learning patterns, allowing content to be tailored to the individual
needs of each student (Zawacki-Richter et al., 2019). Through intelligent
tutoring systems, AI can identify learning difficulties in real time and
provide immediate feedback, facilitating more autonomous and effective learning
(Selwyn, 2022). According to recent studies, AI-based personalization of
learning has shown positive results in knowledge retention and student
motivation (Montiel-Ruiz & López-Ruiz, 2023). However, these innovations
have also raised questions about the reliability of the algorithms and the
possible displacement of the traditional role of the teacher.
While artificial intelligence has established itself
as an innovative tool in academia, its adoption is not without controversy.
(Larson et al., 2024) Recent research has raised concerns about the quality of
AI-mediated learning, the potential dehumanization of teaching, and the
over-reliance on algorithms in educational decision-making (Aoun, 2017). On the
other hand, algorithmic biases in AI systems can generate inequities in assessment
and content recommendation, affecting students with different socioeconomic
profiles (Holmes et al., 2021). Likewise, the lack of training of university
teachers in the use of these technologies represents a significant barrier to
their effective integration in the classroom (Cardona et al., 2023).
The impact of artificial intelligence in higher
education has been addressed from different perspectives in the academic
literature. While some researchers highlight its potential to facilitate
teaching and learning, others warn about the challenges involved in its
implementation in university contexts. For example, Zawacki-Richter et al.
(2019) argue that AI can improve teaching by enabling more personalized and
accessible learning experiences, while Selwyn (2022) argues that its
integration without a regulatory framework and adequate teacher training could
generate more problems than benefits. Along the same lines, Montiel-Ruiz and
López-Ruiz (2023) suggest that the use of AI in higher education should be
accompanied by training strategies for teachers, ensuring that these tools are
used effectively and complement traditional teaching rather than replace it.
This contrast in perspectives evidences the need to deepen the relationship
between the use of AI and the perception that teachers have of its educational
impact. (Otero & Pedraza, 2021).
Another crucial aspect is the transformation of the
role of the university teacher in an increasingly automated educational
environment. Artificial intelligence is not only changing the way knowledge is
imparted, but also the competencies and skills that teachers must develop.
(Salmeron & Torres, 2023) As AI takes over functions traditionally
performed by educators, such as performance assessment or content
personalization, teachers face the challenge of redefining their role as
facilitators of learning in a digitized ecosystem (Montiel-Ruiz &
López-Ruiz, 2023). In this sense, it is imperative to ensure that teacher
training evolves along with technological advances so that educators can take
advantage of AI opportunities without losing the pedagogical essence of
university teaching.
The debate on the integration of AI in higher
education must also address ethical and privacy issues. The collection and
analysis of student data to personalize learning raises concerns about data
protection and the right to privacy (UNESCO, 2023). How can universities ensure
responsible use of AI without compromising student autonomy? What measures
should be implemented to avoid bias in AI systems? These questions are
fundamental to understand the impact of artificial intelligence in education
and establish appropriate regulations for its implementation (Zawacki-Richter
et al., 2019).
Despite the growing adoption of AI in higher
education, doubts persist about how university teachers perceive its impact on
teaching.(Cordón, 2023)While some studies have addressed the benefits of AI in
terms of efficiency and personalization of learning, there is little empirical
evidence on the relationship between the frequency of use of these tools and
teachers' perception of their usefulness in higher education. This generates a
knowledge gap that should be addressed through studies that analyze how the
adoption of AI by teachers affects their perception of the impact of this
technology on teaching. (Perez-Escoda et al., 2020).
In this sense, the present study seeks to answer the
following research question: Is there a relationship between the use of
artificial intelligence and university teachers' perception of its impact on
higher education? Based on this question, the objective of the research is to
analyze the relationship between the use of artificial intelligence and
university teachers' perception of its impact on higher education. To this end,
a quantitative study will be conducted to assess whether there is a significant
correlation between the experience of using AI and the teachers' assessment of
its benefits or limitations in the academic environment. Understanding this
relationship is key for educational institutions, as it will allow the
development of more effective strategies for the integration of AI in teaching,
ensuring that its implementation responds to the real needs of the university
ecosystem and contributes to the improvement of educational processes.
Materials and methods
The present study was designed with the objective of
analyzing the relationship between the use of artificial intelligence tools and
university teachers' perception of their impact on higher education. For this
purpose, the following methodological steps were defined:
A quantitative and correlational approach research
was chosen, since it seeks to measure the relationship between two specific
variables: the use of artificial intelligence and the perception of its
educational impact. This type of study allows establishing statistical
relationships between variables without directly manipulating them
(Hernández-Sampieri & Mendoza, 2018).
The target population is constituted by university
teachers who have incorporated artificial intelligence tools in their
educational practice. Due to the specific nature of the population,
non-probabilistic convenience sampling was used, selecting teachers who
voluntarily participated in the study. This method is common when an accessible
sample is available and relevant information is sought to be obtained
efficiently (Hernández-Sampieri et al., 2014).
A minimum sample size of 100 participants was
established, considering representativeness criteria and the possibility of
performing significant statistical analyses. The inclusion criteria were:
teachers with at least one year of experience in higher education and who have
used an artificial intelligence tool in their teaching work.
For data collection, a structured questionnaire was
designed in digital format, composed of two main sections:
Sociodemographic data: Questions on age, gender, years of teaching
experience and academic area were included.
Level of AI use: Assessed using a scale with four categories: Never,
Rarely, Several times a week and Every day.
Perception of AI impact: Measured with a five-point Likert scale, where 1
represents a very negative perception and 5 a very positive one.
The questionnaire was administered to teachers from
different academic areas and levels of experience. The results indicated that
most teachers have used AI tools in their educational practice, although with
variations in the frequency of use. To ensure the validity of the instrument, a
pilot test was conducted with a small group of participants, which allowed the
questions to be adjusted and clarified before their final application.
Data collection was carried out remotely, using an
online survey platform (Google Forms). Before completing the questionnaire,
participants were provided with a detailed explanation of the objectives of the
study and their informed consent was requested, guaranteeing confidentiality
and the exclusive use of the data for academic purposes.
The data collected were processed and analyzed using
SPSS v30 statistical software. Descriptive statistical tests were performed.
Pearson's correlation coefficient was applied to determine the relationship
between the use of AI and the perception of its impact on higher education.
This coefficient is suitable for measuring the strength and direction of the
linear relationship between two quantitative variables (Hernández-Sampieri
& Mendoza, 2018). A statistical significance level of 0.05 was considered
to assess the relevance of the results obtained.
Results
The present study analyzed the relationship between
the use of artificial intelligence tools and university teachers' perception of
their impact on higher education. The findings obtained from the analysis of
the data collected are presented below.
Table 1. Descriptive statistics on age.
|
Age |
|||||
|
|
Frequency |
Percent |
Valid Percent |
Cumulative
Percent |
|
|
Valid |
1 |
22 |
22.0 |
22.0 |
22.0 |
|
2 |
22 |
22.0 |
22.0 |
44.0 |
|
|
3 |
22 |
22.0 |
22.0 |
66.0 |
|
|
4 |
17 |
17.0 |
17.0 |
83.0 |
|
|
5 |
17 |
17.0 |
17.0 |
100.0 |
|
|
Total |
100 |
100.0 |
100.0 |
|
|
The sample was composed of 100 university teachers, distributed in five age ranges. The most
representative age groups were those under
30 years old (22%), 30-39 years old (22%) and 40-49
years old (22%), which together made up 66% of the sample.
On the other hand, 17%
of the teachers were between 50-59 years old, and another 17%
were 60 years old or older. These
results show a balanced age distribution among the participants, with a higher
representation of young and middle-aged teachers.
Table 2. Descriptive statistics on gender.
|
Genre |
|||||
|
|
Frequency |
Percent |
Valid
Percent |
Cumulative
Percent |
|
|
Valid |
1 |
38 |
38.0 |
38.0 |
38.0 |
|
2 |
27 |
27.0 |
27.0 |
65.0 |
|
|
3 |
35 |
35.0 |
35.0 |
100.0 |
|
|
Total |
100 |
100.0 |
100.0 |
|
|
The sample was composed of 100 university teachers, with a relatively equal distribution in terms of
gender. Thirty-eight
percent of the participants identified
with category 1 (Male), while 27% belonged to category 2
(Female).
On the other hand, 35%
of respondents chose option 3 (I prefer not to say), indicating a considerable proportion of teachers
who chose not to disclose their gender identity.
These results reflect a diversity in the sample and
suggest the importance of considering identity factors in the analysis of
perceptions of artificial intelligence in education.
Table 3. Descriptive statistics on the academic area.
|
Academic_Area |
|||||
|
|
Frequency |
Percent |
Valid
Percent |
Cumulative
Percent |
|
|
Valid |
1 |
26 |
26.0 |
26.0 |
26.0 |
|
2 |
20 |
20.0 |
20.0 |
46.0 |
|
|
3 |
24 |
24.0 |
24.0 |
70.0 |
|
|
4 |
30 |
30.0 |
30.0 |
100.0 |
|
|
Total |
100 |
100.0 |
100.0 |
|
|
The distribution of teachers according to their area
of knowledge showed that 30%
of the participants belonged to
category 4, followed by 26% in category
1. Category
3 teachers accounted for 24%, while category
2 had the lowest representation with 20% of the sample.
These results reflect a balanced participation of
teachers from different disciplines, although with a greater presence in the
academic area represented by category
4.
Table 4. Descriptive statistics on the experience.
|
Experience |
|||||
|
|
Frequency |
Percent |
Valid
Percent |
Cumulative
Percent |
|
|
Valid |
1 |
13 |
13.0 |
13.0 |
13.0 |
|
2 |
15 |
15.0 |
15.0 |
28.0 |
|
|
3 |
20 |
20.0 |
20.0 |
48.0 |
|
|
4 |
18 |
18.0 |
18.0 |
66.0 |
|
|
5 |
21 |
21.0 |
21.0 |
87.0 |
|
|
6 |
13 |
13.0 |
13.0 |
100.0 |
|
|
Total |
100 |
100.0 |
100.0 |
|
|
The analysis of teaching experience shows a wide
variability in the years of teaching in higher education. The largest group
corresponds to category 5 (21%), followed by category
3 (20%) and category
4 (18%).
Less represented were teachers with less than 5 years of experience
(category 1, 13%) and those
with more than 20
years (category 6, 13%). These
results suggest that the majority of teachers in the sample have between 11 and 20 years of experience in higher education.
Table 5. Descriptive statistics on the use of AI.
|
Usage_IA |
|||||
|
|
Frequency |
Percent |
Valid
Percent |
Cumulative
Percent |
|
|
Valid |
0 |
19 |
19.0 |
19.0 |
19.0 |
|
1 |
81 |
81.0 |
81.0 |
100.0 |
|
|
Total |
100 |
100.0 |
100.0 |
|
|
The results show that the vast majority of teachers (81%)
have used artificial intelligence tools in their
teaching practice, while 19% indicated that they have not used AI.
These results evidence a high level of adoption of
artificial intelligence in higher education within the sample analyzed.
Table 6. Descriptive statistics on the frequency of AI use.
|
Frequency_Use_IA |
|||||
|
|
Frequency |
Percent |
Valid
Percent |
Cumulative
Percent |
|
|
Valid |
0 |
19 |
19.0 |
19.0 |
19.0 |
|
1 |
4 |
4.0 |
4.0 |
23.0 |
|
|
2 |
11 |
11.0 |
11.0 |
34.0 |
|
|
3 |
31 |
31.0 |
31.0 |
65.0 |
|
|
4 |
35 |
35.0 |
35.0 |
100.0 |
|
|
Total |
100 |
100.0 |
100.0 |
|
|
Among teachers who reported using AI, the frequency
of use varied considerably. Thirty-five
percent of respondents indicated that they use AI every day (category 4), while 31% reported using it several
times a week (category 3).
Eleven percent use it a
few times a month (category 2), while 4 percent reported using it rarely
(category 1). Finally, 19% of the participants reported not using AI at all (category 0).
These results indicate that, although adoption of AI
is high, its frequency of use varies significantly among teachers, with a trend
toward frequent use.
Table 7. Descriptive statistics of the numerical variables.
|
Descriptive
Statistics |
|||||
|
|
N |
Minimum |
Maximum |
Mean |
Std.
Deviation |
|
Personalization_Learning |
100 |
0 |
4 |
2.45 |
1.553 |
|
Efficient_efficiency_evaluation |
100 |
1 |
5 |
3.75 |
1.192 |
|
Dehumanization_Education |
100 |
1 |
5 |
3.80 |
1.137 |
|
Inequality_Student |
100 |
1 |
5 |
3.70 |
1.049 |
|
Integration_IA |
100 |
1 |
5 |
3.90 |
1.030 |
|
General_Attitude_IA |
100 |
1 |
5 |
4.33 |
1.364 |
|
Valid
N (listwise) |
100 |
|
|
|
|
Analysis of the numerical variables on a scale of 1 to 5 shows a generally positive trend toward artificial intelligence in higher
education. The main findings are presented below:
General Attitude toward AI obtained the highest mean (M = 4.33, SD = 1.364), indicating that most teachers have a favorable
perception regarding the implementation of AI in the educational setting.
Integration of AI in Education also presented a high rating (M = 3.90, SD = 1.030), suggesting that teachers consider the inclusion
of AI in teaching relevant.
Efficient Assessment through AI and Dehumanization
of Learning obtained similar values (M = 3.75, SD = 1.192 and M =
3.80, SD = 1.137, respectively). This
indicates that while teachers recognize the ability of AI to optimize student
assessment, they also perceive a risk of dehumanization in teaching.
Inequality in AI Use had a mean score of 3.70
(SD = 1.049), indicating a moderate
concern about possible inequalities derived from access to these tools.
Personalization of Learning obtained the lowest mean (M = 2.45, SD = 1.553), suggesting that teachers do not perceive as
strongly the ability of AI to personalize learning compared to other dimensions
analyzed.
These results indicate that, in general, teachers
have a positive attitude towards AI and its integration into education,
although concerns related to equity and the impact on human interaction within
the teaching process persist.
Table 8. Pearson correlation
|
Correlations |
|||
|
|
Usage_IA |
General_Attitude_IA |
|
|
Usage_IA |
Pearson Correlation |
1 |
.982** |
|
Sig. (2-tailed) |
|
<.001 |
|
|
N |
100 |
100 |
|
|
General_Attitude_IA |
Pearson Correlation |
.982** |
1 |
|
Sig. (2-tailed) |
<.001 |
|
|
|
N |
100 |
100 |
|
|
**. Correlation is significant at the 0.01 level
(2-tailed). |
|||
A Pearson
correlation analysis was performed to assess the
relationship between the use
of artificial intelligence in teaching
and the general attitude of teachers towards its implementation in higher education.
The results showed a strong and significant positive
correlation between both variables (r = 0.982, p < 0.001). This indicates
that the greater the use of AI tools, the more favorable the teachers' attitude
towards their integration in the educational environment.
Given that the p-value is less than 0.001, it can be
affirmed that the observed relationship is not a product of chance and that
there is a strong association between these two variables. This finding
supports the idea that familiarity with artificial intelligence influences the
positive perception of its benefits in university education.
The results obtained in this study reinforce the
relationship between the use of artificial intelligence (AI) in higher
education and the attitude of teachers towards its implementation. The positive
correlation found indicates that those teachers who use AI more frequently have
a more favorable perception of its educational impact. These findings are
consistent with previous research suggesting that exposure to AI improves
confidence in its pedagogical usefulness (Zawacki-Richter et al., 2019; Cardona
et al., 2023).
However, although the data reflect a generally
positive attitude towards AI, studies such as those by Holmes et al. (2021) and
Selwyn (2022) warn about challenges and limitations in its implementation, such
as the dehumanization of teaching, lack of teacher training, and algorithmic
biases. In this sense, further research is needed to analyze not only the
attitude of teachers, but also the actual effects of AI use on teaching and
learning.
One of the main lines of future research could focus
on assessing the impact of AI on student learning outcomes. So far, most
studies have approached AI from a teaching perspective, but the question of how
the use of AI influences students' knowledge retention, motivation, and
academic performance remains open. Research such as that of Montiel-Ruiz and
Lopez-Ruiz (2023) suggests that AI can enhance personalization of learning, but
more longitudinal studies are needed to measure its long-term effectiveness.
Another key aspect that requires further exploration
is equity in access to AI in higher education. While some universities have
adopted advanced AI tools, other institutions lack the infrastructure and
knowledge to implement them effectively (UNESCO, 2023). Future studies could
analyze the technological gaps between different educational contexts and
propose strategies to reduce them.
It is also essential to continue researching teacher
training in digital competencies and the impact of AI on university pedagogy.
The literature indicates that many teachers still do not have sufficient
technical and pedagogical knowledge to integrate AI effectively in their
classes (Aoun, 2017; Cardona et al., 2023). Future studies could focus on
evaluating teacher training programs in AI and designing effective models for
its implementation in higher education.
Finally, a key question arising from these findings
is to what extent AI should be used in education without compromising human
interaction and traditional pedagogical principles. Future research could
explore the ideal balance between learning automation and the role of the
teacher as facilitator, considering both pedagogical effectiveness and the
ethical and humanistic values of education (Holmes et al., 2021; Selwyn, 2022).
This study contributes to the understanding of the
impact of AI in higher education, but also opens new research questions that
require in-depth exploration. To ensure an effective and ethical implementation
of AI in the university setting, it is essential to continue researching its
impact on learning, equity of access, and teacher training, thus promoting a
use of technology that enhances education without compromising its human
essence.
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Master's
Degree in University Teaching
Universidad Privada César Vallejo, Peru
https://orcid.org/0000-0003-4715-8983
Magister
Statistics
César Vallejo University.
Peru
https://orcid.org/0000-0003-4199-6660
Magister in Metrology
Universidad Privada César Vallejo. Peru
https://orcid.org/0000-0002-4273-2694
Master in Educational Technology
Universidad Privada César Vallejo. Peru