Validation of an Instrument to Measure the
Academic Use of Generative Artificial Intelligence (GenAI) in University
Students
Validación de un instrumento para
medir el uso académico de la IAGen en estudiantes universitarios
Dr. Gilberto Abelino Trejo-Trejo. Universidad Tecnológica de la Selva. México.
Dr. Emmanuel Gordillo-Espinoza. Universidad Tecnológica de la Selva. México.
Received: 2025/09/13 Revised: 2025/11/07 Accepted: 2025/11/30 Published: 2026/01/01
Trejo-Trejo, G.A. & Gordillo-Espinoza, E. (2026). Validación de un instrumento para medir el
uso académico de la IAGen en estudiantes universitarios [Validation of an
Instrument to Measure the Academic Use of Generative Artificial Intelligence
(GenAI) in University Students]. Pixel-Bit,
Revista de Medios y Educación, 75, Art. 7. https://doi.org/10.12795/pixelbit.117960
ABSTRACT
This study aimed to design and
validate an instrument to measure the academic use of generative artificial
intelligence (GenAI) among higher education students. The research was
conducted at the Universidad Tecnológica de la Selva, located in southeastern
Mexico, with a purposive sample of 905 students from various academic
divisions. The initial instrument was developed from a theoretical framework on
digital competence and artificial intelligence, reviewed by nine expert judges,
and pilot-tested. Exploratory and confirmatory factor analyses were applied to
determine the instrument’s structure. Results revealed a seven-dimension model
comprising 42 items, explaining 64% of the total variance, with acceptable
goodness-of-fit indices (CFI = .90; TLI = .90; RMSEA = .06; SRMR = .04) and high
internal consistency (α= .84 and ω= .94). It is concluded that the
instrument demonstrates adequate validity and reliability for assessing
students’ academic engagement with GenAI. However, replication in different
institutional contexts is recommended to test factorial invariance and temporal
stability, ensuring broader applicability in higher education settings.
RESUMEN
Este estudio tuvo como objetivo diseñar y validar un instrumento
para medir el uso académico de la Inteligencia Artificial Generativa (IAGen) en
estudiantes de educación superior. La investigación se desarrolló en la
Universidad Tecnológica de la Selva, en el sureste de México, con una muestra
intencionada de 905 estudiantes de diversas divisiones académicas. El
instrumento inicial fue elaborado a partir de un marco teórico sobre
competencias digitales e inteligencia artificial, sometido al juicio de nueve
expertos y a una prueba piloto. Posteriormente, se aplicaron análisis factorial
exploratorio y confirmatorio para determinar la estructura del instrumento. Los
resultados evidenciaron una solución de siete dimensiones con 42 ítems, que
explicó el 64 % de la varianza total, con índices de ajuste adecuados (CFI =
.90; TLI = .90; RMSEA = .06; SRMR = .04) y una alta consistencia interna
(α=.84 y ω=.94). Se concluye que el instrumento presenta validez y
confiabilidad satisfactorias, aunque se recomienda replicar el estudio en
diferentes contextos institucionales para examinar la invariancia factorial y
la estabilidad temporal.
KEYWORDS · PALABRAS CLAVES
Artificial Intelligence; Educational Technology;
Higher Education; Measurement Instrument; Perception.
Inteligencia Artificial;
Tecnología Educativa; Educación Superior; Instrumento De Medida; Percepción
1. Introduction
In recent years, Artificial Intelligence (AI) tools
have transformed multiple domains, including education, where they are used to
enhance teaching, learning, and institutional management (Bond et al., 2024;
Xia et al., 2024). Russell and Norvig (2021) define AI as a field of study
aimed at developing systems capable of carrying out tasks that require human
intelligence, such as reasoning, perception, and natural language
understanding. Within this field, Generative Artificial Intelligence (GenAI)
constitutes a subset capable of producing new content such as text, images,
music, or code from previously trained data (Jovanović & Campbell,
2022). Its transformative potential in higher education has been widely
recognized (Peres et al., 2023; Ursavaş et al., 2025), offering
opportunities for personalization and creativity in teaching and learning
processes (Fan et al., 2025; Francis et al., 2025).
Tools such as ChatGPT, Gemini, and Copilot have gained
considerable presence in universities due to their ability to generate academic
content and support knowledge management. However, they also pose ethical and
regulatory challenges that require critical reflection on their educational
impact (Romeu et al., 2025; Castaño, 2024). Despite this growing relevance,
empirical literature on how university students perceive and use these
technologies remains limited, making it difficult to fully understand the extent
of their adoption and their potential effects on academic development
(Niño-Carrasco et al., 2025; Ruiz et al., 2024).
Recent systematic reviews highlight that GenAI can
foster personalized learning and the development of advanced digital
competencies, but it also involves risks related to technological dependency
and the quality of generated information (Giannakos et al., 2024). In Latin
America, this research field is still emerging, although interest is increasing
in validating psychometrically robust instruments that assess perceptions and
attitudes toward GenAI (Álvarez-Rebolledo et al., 2019; Maldonado-Suárez &
Santoyo-Telles, 2024; Silgado-Tuñón & López-Flores, 2025).
Within this context, the present study was conducted
at Universidad Tecnológica de la Selva (UTSelva), located in southeastern
Mexico, with students enrolled in Higer University Technitian (TSU) and
bachelor’s degree programs across the academic divisions of Information
Technologies, Administration, Agrobiotechnology, Tourism and Gastronomy, in a
face-to-face modality. This institutional setting offers a relevant scenario
for exploring the academic adoption of GenAI in regional or similar
environments.
The instrument’s design was grounded in a theoretical
model based on digital literacy, technological ethics, and AI-assisted
autonomous learning, incorporating references from the DigCompEdu framework
(Redecker, 2017) and AI literacy (Long & Magerko, 2020). These foundations
gave rise to the seven dimensions of the questionnaire: comprehensive academic
use, content creation and editing, perceived self-efficacy, ethical use, access
and inequalities, environmental impact, and dependence or addiction. This model
enables the assessment not only of the degree of GenAI adoption but also of
students’ critical and reflective maturity regarding its educational
integration.
Thus, the validation of this instrument aims to
contribute to the field of educational innovation by providing a robust tool
for diagnosing and guiding institutional policies on the responsible academic
use of generative artificial intelligence in higher education.
2. Methodology
2.1. Research Design
The study followed a quantitative, instrumental
research design aimed at analyzing the psychometric properties of the
questionnaire (Ato et al., 2013). The process adhered to international
standards for educational and psychological testing (American Educational
Research Association, American Psychological Association & National Council
on Measurement in Education, 2018), which included a theoretical review, expert
judgment, and empirical validation through factorial analyses.
2.2. Participants
The sample consisted of 905 students (460 men, 439
women, and 6 unspecified) from Universidad Tecnológica de la Selva, a public
institution in southeastern Mexico offering Higer University Technitian (HUT)
and bachelor’s degree programs in face-to-face modality. Participants belonged
to the academic divisions of Information Technologies, Administration,
Agrobiotechnology, Tourism and Gastronomy. A purposive non-probabilistic
sampling strategy was used. Inclusion criteria were: enrollment during 2025,
voluntary participation, and completion of the questionnaire. Incomplete or
duplicate responses were excluded.
2.3. Instrument
The initial instrument consisted of 47 items
distributed across 9 dimensions, developed from the theoretical model described
in the Introduction. After being evaluatedevaluation by nine experts
(Escobar-Pérez & Cuervo-Martínez, 2008), items with Aiken’s V < .80, or
considered redundant or ambiguous, were removed. As a result, a revised version
of 45 items was retained for the pilot test.
Subsequently, the exploratory factor analysis (EFA)
suggested a seven-factor structure with 42 items, which was maintained in the
final version (Table 1). The response scale was a 5-point Likert format (1 =
Strongly disagree, 2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, 5 =
Strongly agree).
Table 1
Instrument Version
Traceability
|
Stage |
Number of items |
Number of
dimensions |
Criteria for
Modification |
Main Outcome |
|
Initial version |
47 |
9 |
Theoretical review and initial drafting based on
DigCompEdu and AI literacy |
First conceptual proposal |
|
Expert Judgment |
45 |
9 |
Elimination of items with V < .80 and
redundant items; wording adjustments; item reclassification |
Version for pilot testing |
|
Pilot Test (EFA) |
45 |
9 → 7 |
Grouping of conceptually related factors and
removal of items with loadings < .40 |
Adjusted empirical structure |
|
Final Version |
42 |
7 |
Model confirmation through CFA and internal
consistency analysis |
Validated instrument |
Note: Arranged by the authors.
2.4. Validated Procedure
Phase 1. Content Validity: The
initial 47-item questionnaire was evaluated by a panel of nine expert judges:
five men and four women; seven from Mexico and two from Colombia. Six held
doctoral degrees and three held master’s degrees. Their research areas included
data mining, artificial intelligence, educational innovation, generative AI,
ICTs, and data science. Professional experience ranged from 12 to 38 years, and
scientific publications from 4 to 25, indicating a group with extensive
academic and research backgrounds.
Experts evaluated each item in terms of clarity,
relevance, pertinence, and sufficiency, using a four-point scale, and provided
qualitative feedback through a rubric adapted from Escobar-Pérez &
Cuervo-Martínez (2008). Aiken’s Content Validity Coefficient (V) (Aiken, 1985;
Escurra, 1988) was calculated using the following formula (Martín-Romera &
Molina, 2017):
Where: 
mean rating of judges
lowest possible score
number of scale categories
Phase 2. Pilot test: The
sample size used is justified based on psychometric standards. However, when
conducting factor analyses, several authors recommend between 5 and 10
participants per item (Hair et al., 2019; Lloret-Segura et al., 2014).
Considering the 45 initial items, the ideal sample should range between 230 and
450 cases. In this study, data were collected from 905 students, which ensures
a robust level of reliability. Likewise, the use of the JASP software enabled
the application of maximum likelihood models and the computation of
goodness-of-fit indices widely employed in the literature, with the advantage
of being an open-access tool that promotes reproducibility.
The instrument was administered to students from the
Universidad Tecnológica de la Selva over a two-week period through a Google
Forms survey. It is worth noting that the first section of the form emphasized
the principles of anonymity, confidentiality, and the scientific managment of
the data.
Phase 3. Exploratory Factor Analysis (EFA) and
Confirmatory Factor Analysis (CFA) were applied to validate construct
structure. The process followed internationally recognized psychometric
standards (American Educational Research Association et al., 2018). Additionally,
the instrument was aligned with contemporary research emphasizing the need to
measure self-efficacy, digital ethics, environmental impact, and technological
dependence in academic contexts involving GenAI (Giannakos et al., 2024;
Silgado-Tuñón & López-Flores, 2025).
Phase 4. Reliability: Internal consistency was
calculated using Cronbach’s 
and McDonald 
.
Phase 5. Final instrument: The final validated
instrument assesses students’ experiences, skills, and perceptions regarding
the academic use of GenAI in higher education.
Although analyses of factorial
invariance and temporal stability (test–retest) were not included in the
present study, future research will incorporate these components using broader
and more diverse samples. These analyses would assess whether the factorial
structure remains stable across groups (e.g., gender, academic area) and over
time. Future studies will also explore convergent and discriminant validity to
compare the constructs with theoretically related or distinct measures.
Incorporating these analyses will enhance the instrument’s validity,
generalizability, and psychometric robustness.
3. Analysis and
results
Content validity assessed
through Aiken’s V showed adequate values for most items, with coefficients
ranging from .80 to .95, evidencing clarity, relevance, and appropriateness in
item wording (Table 2).
Table 2
Aiken’s V Coefficients by
Category and Item
|
Dimension |
item |
Clarity |
Coherence |
Relevance |
Sufficiency |
|
1. Information Search and Management |
1 |
0.93 |
0.93 |
0.89 |
0.89 |
|
2 |
0.81 |
0.85 |
0.89 |
||
|
3 |
0.85 |
0.81 |
0.85 |
||
|
4 |
0.81 |
0.81 |
0.89 |
||
|
5 |
0.89 |
0.89 |
0.93 |
||
|
6 |
0.93 |
0.93 |
0.96 |
||
|
7 |
0.96 |
0.93 |
0.89 |
||
|
2. Academic Tutoring and Assistance |
8 |
0.85 |
0.85 |
0.85 |
0.89 |
|
9 |
0.93 |
0.89 |
0.96 |
||
|
10 |
0.93 |
0.96 |
0.89 |
||
|
11 |
0.85 |
0.85 |
0.85 |
||
|
3. Content Creation and Editing |
12 |
0.93 |
0.85 |
0.96 |
0.93 |
|
13 |
0.93 |
0.78* |
0.85 |
||
|
14 |
0.89 |
0.85 |
0.93 |
||
|
15 |
0.96 |
0.93 |
0.93 |
||
|
4. Perceived Self-Efficacy |
16 |
0.81 |
0.85 |
0.89 |
0.96 |
|
17 |
0.93 |
0.96 |
0.96 |
||
|
18 |
0.85 |
0.89 |
0.89 |
||
|
19 |
0.93 |
0.93 |
0.93 |
||
|
20 |
0.89 |
0.93 |
0.96 |
||
|
21 |
0.93 |
0.89 |
0.85 |
||
|
22 |
0.93 |
0.93 |
0.93 |
||
|
5. Ethical Use |
23 |
0.93 |
0.96 |
1.00 |
1.00 |
|
24 |
0.93 |
0.96 |
0.93 |
||
|
25 |
0.85 |
0.89 |
0.89 |
||
|
26 |
1.00 |
0.96 |
1.00 |
||
|
27 |
1.00 |
0.96 |
0.96 |
||
|
28 |
0.96 |
0.89 |
0.93 |
||
|
29 |
0.93 |
0.89 |
0.96 |
||
|
6. Limitations and Barriers |
30 |
0.85 |
0.85 |
0.89 |
0.93 |
|
31 |
0.89 |
0.89 |
0.85 |
||
|
32 |
0.81 |
0.81 |
0.81 |
||
|
7. Accessibility and Equity |
33 |
0.85 |
0.89 |
0.89 |
0.93 |
|
34 |
0.85 |
0.93 |
0.89 |
||
|
35 |
0.85 |
0.89 |
0.78* |
||
|
36 |
0.85 |
0.85 |
0.85 |
||
|
8. Environmental Impact |
37 |
0.89 |
0.96 |
0.89 |
0.96 |
|
38 |
0.85 |
0.78* |
0.85 |
||
|
39 |
0.85 |
0.85 |
0.85 |
||
|
40 |
0.85 |
0.85 |
0.85 |
||
|
41 |
0.81 |
0.85 |
0.85 |
||
|
42 |
0.74* |
0.78* |
0.81 |
||
|
9. Dependence or Addiction |
43 |
0.85 |
0.81 |
0.81 |
0.96 |
|
44 |
0.81 |
0.78* |
0.81 |
||
|
45 |
0.81 |
0.81 |
0.78* |
||
|
46 |
0.78* |
0.74* |
0.81 |
||
|
47 |
0.74* |
0.74* |
0.74* |
Note: Asterisks (*) indicate
items with Aiken’s V < .80 in at least one category, later revised in
wording.
Based on expert feedback and Aiken’s V results, items
2 and 44 were removed due to conceptual redundancy. Item 13, which scored
slightly below .80 in coherence, was rewritten in the refinement stage. Item 47
was removed due to values below .80 in three categories. Item 6 was reassigned
to the Content Creation and Editing dimension, while items 13, 35, 38, and 42
were reformulated based on expert recommendations. The resulting 45-item
version was applied in the pilot test with 905 students.
Exploratory Factor Analysis (EFA) confirmed data suitability (KMO = excellent;
Bartlett’s test = significant), indicating strong factorability. Although the
theoretical model proposed nine dimensions, EFA suggested a seven-factor
solution, explaining 64% of total variance. Three items (25, 33, and 36) were
removed due to low factor loadings (< .40). Item 14 was reassigned to the
Perceived Self-Efficacy dimension. The factors “Limitations and Barriers” and
“Accessibility and Equity” merged into a single dimension.
Factor loadings ranged from
.44 to .96, using oblique rotation (Promax), with no significant cross-loadings
(> .30). Communalities ranged from .41 to .79, indicating solid contribution
of items to their respective factors.
Subsequently, the Confirmatory Factor Analysis (CFA) compared the original
nine-factor model with the empirically derived seven-factor model. Results
indicated superior global fit for the seven-dimension model (CFI = .90; TLI =
.90; RMSEA = .06; SRMR = .04); these results confirm the construct validity of the
revised seven-dimension model, reflecting students’ experiences and perceptions
regarding the academic use of GenAI more accurately than the original
formulation.
The internal consistency was raised to Cronbach’s α and McDonald’s ω
ranged from .84 to .94, demonstrating high internal consistency and measurement
stability.
Taken together, the analyses support that the final
structure comprising seven dimensions and 42 items constitutes a parsimonious
and robust representation of the construct Academic Use of Generative
Artificial Intelligence among University Students (Table 3). Each
modification—whether item removal, relocation, or merging—was guided by
statistical and conceptual criteria, with the aim of maximizing the
instrument’s theoretical coherence and empirical validity.
Table 3
Final Version of the Instrument
|
Dimension |
Item |
|
Comprehensive Academic Use |
1. I
use of GenAI tools to search for academic information. |
|
2. I
use of GenAI tools to analyze academic materials such as PDF reports, videos,
statistical data, and others. |
|
|
3. I
use of GenAI tools to cite and/or generate bibliographic references in APA,
MLA, Chicago, IEEE, or Vancouver formats. |
|
|
4. I
use of GenAI tools to translate and understand academic texts in other
languages. |
|
|
5. I
use of GenAI tools to generate or structure ideas, outlines, or arguments for
academic assignments. |
|
|
6. I
use of GenAI tools on a daily basis to address academic questions. |
|
|
7. I
use of GenAI tools to check grammar, spelling, and writing style in my
academic work. |
|
|
8. I
use of GenAI tools to solve or request help with complex topics when studying
independently. |
|
|
9. I
use of GenAI tools to prepare for exams. |
|
|
Content creation adn editing |
10.
I use GenAI tools to create summaries of academic texts. |
|
11.
I use GenAI tools to generate ideas, texts, or slides for academic
presentations. |
|
|
12.
I use GenAI tools to write and/or edit academic assignments. |
|
|
13. I use GenAI tools to generate multimedia content
(videos, images, audio) for academic activities. |
|
|
Perceived Self-Efficacy |
14. I adapt and combine GenAI-generated responses with my own ideas when
completing academic assignments. |
|
15.
I feel confident using GenAI tools to search for information, write texts, or
solve academic questions. |
|
|
16.
I can learn to use new GenAI tools quickly if necessary. |
|
|
17.
I trust my ability to solve academic problems using GenAI tools. |
|
|
18.
I feel competent in using GenAI tools to improve my learning. |
|
|
19.
I can use GenAI tools to enhance the quality of my academic work. |
|
|
20.
I feel capable of evaluating the quality of information generated by GenAI
tools. |
|
|
21.
I trust my ability to effectively integrate GenAI tools into my study
routine. |
|
|
Ethical Use |
22.
I understand how to use GenAI tools appropriately and ethically in my
studies. |
|
23.
I verify the reliability of information and sources generated by GenAI tools. |
|
|
24.
I evaluate whether the use of GenAI tools improves my learning. |
|
|
25.
I consider GenAI a complementary tool rather than a substitute. |
|
|
26.
I recognize that GenAI tools may produce incorrect results or
interpretations. |
|
|
27.
I am aware of the risks that GenAI tools may pose in academic contexts. |
|
|
28.
I am aware of the risks that GenAI tools may pose in personal contexts. |
|
|
Access and inequality |
29.
I have experienced technical limitations when using GenAI tools for my
studies (e.g., connectivity issues, device compatibility, lack of licenses,
platform access failures). |
|
30.
I have encountered language barriers when using GenAI tools. |
|
|
31.
Lack of knowledge on how to use or configure GenAI tools is a barrier for me. |
|
|
32.
I have had difficulties accessing GenAI tools due to subscription
limitations. |
|
|
33.
I know classmates who cannot use GenAI tools due to lack of adequate
technology. |
|
|
Enviromental impact |
34.
I am aware that intensive use of GenAI tools implies high electricity
consumption. |
|
35.
I inform myself about the environmental effects of using GenAI tools. |
|
|
36.
I consider the ecological impact of intensive GenAI use. |
|
|
37.
I reflect on how the academic use of GenAI tools may contribute to climate
change. |
|
|
38.
I agree with promoting responsible use to reduce the environmental impact of
GenAI tools. |
|
|
39.
I am willing to reduce my use of GenAI tools to lower their ecological
footprint. |
|
|
Dependence or addiction |
40.
I feel that I frequently rely on GenAI tools to complete academic tasks. |
|
41.
I use GenAI tools even when they are not necessary for my academic
activities. |
|
|
42.
I have noticed that I spend more time than necessary using GenAI tools for my
studies. |
Note: Arranged by the authors.
4. Discussion
The results of the validation process provide strong
evidence of the internal consistency and construct validity of the instrument
designed to measure the academic use of generative artificial intelligence
(GenAI) in higher education. The final structure of seven dimensions and 42
items reflects a parsimonious and theoretically coherent model, aligned with
the digital competence and AI literacy frameworks proposed by Redecker (2017)
and Long and Magerko (2020).
The dimensional reduction
from nine to seven factors does not represent a conceptual loss but rather a
theoretical consolidation that groups related components and enhances the
interpretability of the instrument. For instance, the integration of the dimensions
Limitations and Barriers with Accessibility and Equity suggests that both
constructs converge on a shared notion of contextual conditions for the
critical appropriation of GenAI, which is consistent with recent findings on
digital inequality and technological access (Giannakos et al., 2024). Likewise,
the strengthening of the Perceived Self-Efficacy dimension highlights the
importance of technological competence beliefs in the responsible adoption of
generative tools (Qadir, 2023).
From an applied perspective, the instrument makes it
possible to diagnose the level of GenAI literacy and academic use among
university students, offering valuable information for designing institutional
strategies for ethical, technical, and reflective training in AI use. This
potential for practical application aligns with the need for universities to
regulate and guide the use of GenAI in educational and assessment processes
(Bond et al., 2024; Holmes et al., 2019).
The importance of advancing toward studies that
examine the factorial invariance of the instrument is also recognized, with the
aim of determining whether the seven-dimension structure remains stable across
different comparison groups such as gender, academic area, or educational level
(Technical Degree and Bachelor’s Degree). Incorporating these analyses—along
with tests of convergent and discriminant validity—will allow for the
evaluation of the model’s metric and structural equivalence, strengthening evidence
of external validity and result generalizability. Such procedures, widely
recommended in contemporary psychometrics (Milfont & Fischer, 2010; Putnick
& Bornstein, 2016), will consolidate the potential of the instrument as a
standardized tool for comparative and longitudinal research in higher
education.
Finally, the item refinement process and the
establishment of a robust factor structure support the utility of the
instrument as both a diagnostic and research tool. Its application can
contribute to the empirical understanding of the role of GenAI in higher
education, particularly in the development of critical digital competencies,
ethical reasoning, and students’ academic autonomy. In sum, the study offers a
relevant methodological and conceptual advancement, albeit with the necessary
caution regarding its scope and the need for additional validation efforts.
5. Conclusions
The present study successfully designed and validated
a reliable and valid instrument to measure the academic use of generative
artificial intelligence (GenAI) among higher education students. The final
structure comprising 42 items across seven dimensions, demonstrated adequate
factorial fit, internal consistency, and theoretical coherence, supporting its
applicability for educational research and institutional management.
Operationally, the instrument allows for the
calculation of dimension scores through the mean of responses from 1 to 5 on
the Likert scale. The following interpretive ranges are recommended: 1.00 to
2.49 (low level), 2.50 to 3.49 (medium level), and 3.50 to 5.00 (high level).
These scores may be used to identify strengths and areas for improvement in
students’ academic, ethical, and critical use of GenAI, as well as to inform
training strategies or institutional policies related to digital literacy and technological
ethics.
The instrument is suitable for
institutional diagnostic studies, comparative evaluations across programs or
academic divisions, and longitudinal monitoring of digital competence
development. Its implementation can support decision-making in universities seeking
to integrate AI responsibly into teaching and learning processes.
However, it is important to
note that the study’s findings are limited to a single technological university
in southeastern Mexico. Therefore, results should not be generalized without
caution to other educational contexts. Future research should incorporate
factorial invariance testing, convergent and discriminant validity analyses,
and temporal stability assessments to strengthen the generalizability and
applicability of the instrument across diverse contexts.
In summary, this study offers
a significant methodological and practical contribution to the field of
educational innovation by providing a robust tool for understanding and
promoting the reflective and ethical academic use of GenAI in higher education.
Suppelemntary
material
The dataset used in this study is available upon
reasonable request to the corresponding author.
Conflict of interest
The authors declare no conflict of interest..
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