Vol. 7 – Núm. 1 / Enero - Junio – 2026
Acceptance of artificial intelligence tools as support for English language
learning among first-level university students
Aceitação de ferramentas de inteligência artificial como apoio à aprendizagem da
língua Inglesa entre estudantes universitários de primeiro nivel
Aceptación de herramientas de inteligencia artificial como apoyo para el
aprendizaje del idioma Inglés en estudiantes universitarios de primer nivel
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Sotomayor Cantos Karina Fernanda
1
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Universidad Técnica Estatal de Quevedo
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k
sotomayorc
@uteq.edu.ec
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https://orcid.org/0000-0002-6134-1875
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Baños Coello María Belén
2
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Universidad Técnica Estatal de Quevedo
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mbanos@uteq.edu.ec
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https://orcid.org/0000-0002-7312-3058
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Camacho León Luis Patricio
3
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Universidad Bolivariana del Ecuador
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luis.camacho.leon@gmail.com
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https://orcid.org/0009-0004-1219-5674
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Camacho Castillo Luis Alfredo
4
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Universidad Técnica Estatal de Quevedo
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l
camacho
@uteq.edu.ec
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https://orcid.org/0000-0003-1192-2804
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Como citar:
Peralta Fajardo, M, E., Guerrero Bravo, M, A., Mera Delgado, R, V. & Zambrano Guerrero,
J, P. (2026). Adaptaciones pedagógicas en el aula para la inclusión de niños de cinco años
con Trastorno del Espectro Autista.
Código Científico Revista de Investigación, 7(1), 3485–
3509.
Recibido:
24/05/2026
Aceptado:
22/06/2026
Publicado:
30/06/2026
3485
Abstract
The
integration
of
artificial
intelligence
(AI)
into
higher
education
has
created
new
opportunities to support English language learning. However, students' acceptance of these
technologies is an important factor in determining their effective use. This study aimed to
analyze the acceptance of AI tools as support for English language learning among first-level
university students at the Universidad Técnica Estatal de Quevedo. A quantitative, descriptive,
and
non-experimental
research
design
was
employed.
Data
were
collected
through
a
questionnaire administered to 80 students from three first-level parallel classes. The instrument
examined three dimensions based on the Technology Acceptance Model (TAM): perceived
usefulness, perceived ease of use, and intention to use. A pilot test was conducted to assess the
reliability
of
the
instrument,
and
Cronbach's
alpha
coefficients
indicated
high
internal
consistency. The results showed that perceived usefulness obtained the highest mean score (M
= 3.45), followed by perceived ease of use (M = 3.375) and intention to use (M = 3.26875).
The complete instrument demonstrated high internal consistency (
α
= 0.957). These findings
indicate
that
students
generally
perceive
AI
tools
as
useful
and
relatively
easy
to
use
for
supporting their English learning. However, the comparatively lower score for intention to use
suggests that recognizing the benefits of AI does not necessarily translate into an equally strong
intention to use these tools. The study concludes that AI has potential as a complementary
resource
for
English
language
learning,
provided
that
its
integration
is
accompanied
by
appropriate pedagogical guidance and responsible use.
Keywords:
Artificial
Intelligence;
English
language
learning;
Technology
acceptance;
Perceived usefulness; Perceived ease of use, Intention to use.
Resumen
La integración de la inteligencia artificial (IA) en la educación superior ha generado nuevas
oportunidades
para
apoyar
el
aprendizaje
del
inglés.
Sin
embargo,
la
aceptación
de
estas
tecnologías por parte del alumnado es un factor importante para determinar su uso efectivo.
Este estudio tuvo como objetivo analizar la aceptación de herramientas de IA como apoyo para
el aprendizaje del inglés entre estudiantes de primer ciclo de la Universidad Técnica Estatal de
Quevedo. Se empleó un diseño de investigación cuantitativo, descriptivo y no experimental.
Los datos se recopilaron mediante un cuestionario aplicado a 80 estudiantes de tres clases
paralelas de primer ciclo. El instrumento examinó tres dimensiones basadas en el Modelo de
Aceptación de la Tecnología (TAM): utilidad percibida, facilidad de uso percibida e intención
de uso. Se realizó una prueba piloto para evaluar la fiabilidad del instrumento, y los coeficientes
alfa
de
Cronbach
indicaron
una
alta
consistencia
interna.
Los
resultados
mostraron
que
la
utilidad percibida obtuvo la puntuación media más alta (M = 3,45), seguida de la facilidad de
uso percibida (M = 3,375) y la intención de uso (M = 3,26875). El instrumento completo
demostró una alta consistencia interna (α = 0,957). Estos hallazgos indican que los estudiantes
generalmente perciben las herramientas de IA como útiles y relativamente fáciles de usar para
apoyar su aprendizaje del inglés. Sin embargo, la puntuación comparativamente más baja en la
intención de uso sugiere que reconocer los beneficios de la IA no se traduce necesariamente en
una intención igualmente fuerte de usar estas herramientas. El estudio concluye que la IA tiene
potencial como recurso complementario para el aprendizaje del idioma inglés, siempre que su
integración vaya acompañada de una orientación pedagógica adecuada y un uso responsable.
Palabras
clave:
Inteligencia
artificial;
Aprendizaje
del
idioma
inglés;
Aceptación
de
la
tecnología; Utilidad percibida; Facilidad de uso percibida; Intención de uso.
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3486
Resumo
A integração da inteligência artificial (IA) no ensino superior criou novas oportunidades para
apoiar o aprendizado da língua inglesa. No entanto, a aceitação dessas tecnologias pelos alunos
é um fator importante para determinar seu uso eficaz. Este estudo teve como objetivo analisar
a aceitação de ferramentas de IA como suporte para o aprendizado da língua inglesa entre
alunos do primeiro ano da Universidade Técnica Estadual de Quevedo. Foi empregado um
delineamento
de
pesquisa
quantitativo,
descritivo
e
não
experimental.
Os
dados
foram
coletados
por
meio
de
um
questionário
aplicado
a
80
alunos
de
três
turmas
paralelas
do
primeiro ano. O instrumento examinou três dimensões com base no Modelo de Aceitação da
Tecnologia (TAM): utilidade percebida, facilidade de uso percebida e intenção de uso. Um
teste piloto foi realizado para avaliar a confiabilidade do instrumento, e os coeficientes alfa de
Cronbach
indicaram
alta
consistência
interna.
Os
resultados
mostraram
que
a
utilidade
percebida obteve a maior pontuação média (M = 3,45), seguida pela facilidade de uso percebida
(M = 3,375) e pela intenção de uso (M = 3,26875). O instrumento completo demonstrou alta
consistência interna (α = 0,957).
Esses resultados indicam que os alunos geralmente percebem
as ferramentas de IA como úteis e relativamente fáceis de usar para apoiar seu aprendizado de
inglês. No entanto, a pontuação comparativamente menor para a intenção de uso sugere que
reconhecer os benefícios da IA não se traduz necessariamente em uma intenção igualmente
forte de usar essas ferramentas. O estudo conclui que a IA tem potencial como um recurso
complementar
para
o
aprendizado
da
língua
inglesa,
desde
que
sua
integração
seja
acompanhada por orientação pedagógica adequada e uso responsável.
Palavras-chave
:
Inteligência
artificial;
Aprendizado
da
língua
inglesa;
Aceitação
da
tecnologia; Utilidade percebida; Facilidade de uso percebida; Intenção de uso.
Introduction
Artificial intelligence (AI) has been advancing at a fast pace, and this progress has
already reshaped several aspects of education, particularly the way teachers and students find,
process, and use information. Generative AI tools in particular have opened new possibilities
for supporting teaching and learning, although they have also raised legitimate questions about
how
to
bring
them
into
educational
settings
in
a
way
that
is
effective,
responsible,
and
sustainable. Within English language learning specifically, these tools give students the chance
to practice their language skills, get immediate feedback, receive support tailored to their needs,
and interact with digital resources at their own pace (Wei, 2023; Walter, 2024).
This is part of why AI has drawn growing interest within English as a Foreign Language
(EFL)
education,
since
it
has
the
potential
to
complement,
rather
than
replace,
traditional
teaching practices.
Tools powered by AI can support learning activities related to writing,
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3487
vocabulary, grammar, pronunciation, and communication. Recent studies point out that AI can
help boost students’ motivation, encourage self
-regulated learning, and enrich their overall
language learning experience (Wei, 2023). Along the same lines, Guan et al. (2025) argue that
generative AI works best as a compliment to the interaction between teachers and students, not
as a replacement for the teacher's role.
Even with all this potential, simply introducing AI education does not guarantee the
students
will
accept
or
use
it.
Whether
they
do
largely
depend
on
how
they
perceive
its
usefulness, accessibility, reliability, and the risk it might involve. Davis (1989) address this
question
through
the
Technology
Acceptance
Model
(TAM),
Proposing
that
perceived
usefulness and perceived ease of use are the two key factors shaping whether people accept a
given technology. These two ideas still hold up in more recent research examining how AI is
being used in educational contexts (Ibrahim et al., 2025).
Several recent studies confirm that technology acceptance models remain useful for
understanding how AI is being adopted in higher education. Acosta-Enriquez et al. (2024), for
example,
point
to
a
range
of
technological
and
contextual
factors
that
help
explain
why
university students accept AI. Mustofa et al. (2025) add that trust, ethics, and subjective norms
also
shape
a
student's
willingness
to
adopt
AI
tools.
Within
English
language
learning
specifically,
other
researchers
have
looked
at
how
motivation
and
individual
differences
influence students' willingness and intention to use AI-support technologies (Sun et al., 2025).
That said, bringing AI effectively into English language education is not without its
challenges. Both students and teachers need a solid level of AI literacy and critical thinking to
evaluate and use AI-generate content appropriately. Walter (2024) stresses that AI literacy,
prompt
engineering,
and
critical
thinking
are
all
essential
for
integrating
these
tools
into
education
responsibly.
Almehmadi
(2024)
Also
notes
that,
with
so
many
AI
tools
now
available, educators face the added challenge of choosing which ones actually fit their specific
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pedagogical goals.
All of this suggests that understanding
how
a student
accepts
AI is an
important step before pushing for its broader integration into English language learning.
While
AI
acceptance
has
already
been
studied
in
higher
education
and
language
learning more broadly, less is known about how university students in specific institutional
contexts perceive AI tools as support for English language learning. Gaining a clearer picture
of these perceptions can give teachers and institutions valuable insight as they work to bring
AI into their teaching practices in a meaningful and responsible way.
With
this
in
mind,
the
present
study
aims
to
analyze
the
acceptance
of
artificial
intelligence tools as support for English language learning among first-level university students
at
the
Universidad
Técnica
Estatal
de
Quevedo.
To
reach
this
goal,
it
examines
three
dimensions drawn from the Technology Acceptance Model: perceived usefulness, perceived
ease of use, an intention to use. Taken together, these dimensions are meant to offer a clearer
picture of how students perceive AI tools and to contribute to a better understanding of how AI
might be integrated into English language learning in higher education.
Theoretical Framework
Artificial Intelligence in Education and in Teaching and Learning
Teaching
and
learning
nowadays
has
a
novelty
that
was
thought
first
just
as
experimental, however, artificial intelligence (AI) has come to constitute a relevant element in
educational environments. Ever since the world was shaken by the emergence of AI tools, Chat
GPT for instance, back in 2022, educational institutions started to reconsider important aspects
such as policies, methodologies, and even the manner in which instructors and also learners
interact when using technology (Walter, 2024).
Nevertheless,
institutions
are
trying
to
manage
the
situation.
While
some
of
them
restrict their use, some others are working hard to find a way to incorporate them in their
practices. Still, further study is needed in order to understand the elements that influence both
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acceptance and proper use people can make of it.
Personalized learning, quick feedback, and assistance for students with various learning
requirements
are
just
a
few
of
the
significant
educational
opportunities
that
AI
presents.
However, proper digital and AI literacy among educators and learners is also necessary for its
successful incorporation (Walter, 2024). Therefore, these technologies' instructional usefulness
rests not just on their technical prowess but also on users' capacity to use them responsibly and
critically.
AI-supported solutions can offer chances for language practice, feedback, and access
to educational materials in the context of English as a Foreign Language (EFL). Wei (2023)
found that self-regulated learning, L2 motivation, and English learning achievement were all
positively correlated with AI-mediated teaching. In a similar vein, Guan et al. (2025) highlight
that generative AI can serve as a supplementary component in the interaction between teachers,
students, and technology rather than taking the place of teachers through a mixed-methods
study in EFL instruction.
Despite these opportunities, integrating AI is not without its difficulties. While teachers
may struggle to choose the right tools and establish pedagogically effective uses, students may
grow unduly reliant on AI-generated solutions. Walter (2024) emphasizes the significance of
critical
thinking,
rapid
engineering,
and
AI
literacy
for
appropriate
integration.
Similarly,
Almehmadi (2024) notes that as the number of AI tools increases, it becomes more crucial for
teachers to decide which technologies are suitable for particular teaching and learning goals.
Determining the potential function of AI tools as complementary resources in English
language
acquisition
thus
requires
a
knowledge
of
how
students
view
and
embrace
these
technologies.
Acceptance of Educational Technologies and the Technology Acceptance Model (TAM)
One of the most popular theoretical frameworks for understanding users' acceptance of
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information technology is the Technology Acceptance Model (TAM), which was first put forth
by Davis in 1989. According to the concept, perceived utility and perceived ease of use are two
basic ideas that impact technology acceptance.
According to Davis (1989), perceived usefulness is the degree to which an individual
believes that utilizing a specific technology will enhance their performance, while perceived
ease of use is the degree to which an individual believes that utilizing the technology will
require minimal effort. Users' attitudes and behavioral intentions toward technology use are
influenced by these beliefs.
In studies on AI acceptance, the TAM is still pertinent. When Ibrahim et al. (2025)
adapted the concept to artificial intelligence, they discovered that users' opinions toward AI are
still largely explained by perceived utility. In a similar vein, Zhang et al. (2023) looked at
aspects related to AI acceptability in their research of pre-service teachers and emphasized the
importance of users' views in influencing their desire to use these technologies.
Traditional technology acceptance models have also been extended in recent research
to include more variables. Acosta-Enriquez et al. (2024) highlight the significance of elements
like social influence, perceived value, hedonic motivation, and habit in comprehending AI
acceptance
in
university
environments
through
a
conceptual
analysis
based
on
UTAUT2
(Unified Theory of Acceptance and Use of Technology 2). Additionally, Wu et al. (2022) show
that students' acceptance of AI-assisted learning settings can be influenced by perceived risks
and advantages.
Other research has integrated psychological, ethical, and social aspects into models of
technology acceptance. According to Mustofa et al. (2025), students' acceptance of AI tools is
influenced by subjective norms, ethics, and trust. In a similar vein, Xu et al. (2024) discovered
that educators' intention to employ AI tools is significantly influenced by effort expectation,
which is conceptually linked to perceived ease of use.
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The current study concentrates on the fundamental TAM elements that are most closely
associated
with
the
research
goal,
even
though
these
expanded
models
offer
more
comprehensive explanations of technology acceptance. Therefore, three factors were chosen to
analyze students' adoption of AI technologies as help for English language learning: perceived
utility, perceived ease of use, and intention to use.
Perceived Usefulness, Perceived Ease of Use, and Intention to Use
Perceived Usefulness
A key concept in the TAM is perceived utility, which describes how much consumers
think a technology may help them accomplish particular goals or enhance their performance
(Davis, 1989).
When
AI
tools
support
tasks
like
writing,
grammar,
vocabulary,
pronunciation,
communication, or access to explanations and feedback, students may view them as helpful
when learning English. Wei (2023) found good correlations between AI-supported instruction
and a number of learning-related outcomes, highlighting the potential utility of AI in language
learning.
According to more recent studies, acceptance of AI technologies can still be explained
by perceived utility. While Mansoor et al. (2026) identify a number of external factors that may
influence students' attitudes toward AI acceptance in English writing, including digital self-
efficacy,
institutional
support,
and
technological
literacy,
Ibrahim
et
al.
(2025)
found
that
perceived usefulness plays a significant role in users' attitudes toward AI.
As a result, perceived usefulness is a crucial factor in determining whether students
view AI as a beneficial supplemental tool for learning English.
Perceived Ease of Use
The degree to which consumers think using a technological system is simple and does
not take a lot of work, is known as perceived ease of use (Davis, 1989). Students may be more
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3492
inclined to integrate accessible and user-friendly technologies into their learning processes in
educational settings.
Studies on AI acceptance have shown how relevant this notion is. While Xu et al. (2024)
discovered that effort anticipation was a significant factor linked to educators' intention to
employ AI tools, Zhang et al. (2023) investigated acceptability among pre-service instructors.
However, successful educational use is not always ensured by ease of use. In order to
assess the data produced by these technologies, students must also have adequate AI literacy
and critical thinking abilities. While Almehmadi (2024) emphasizes the difficulty of choosing
appropriate
AI
tools
for
particular
educational
purposes,
Walter
(2024)
stresses
that
users
require the right abilities to interact with AI responsibly.
Perceived simplicity of use is therefore important since it can influence students' desire
to integrate AI technologies into their
English learning activities in addition to potentially
facilitating technology acceptance.
Intention to Use
The willingness or intended propensity of an individual to utilize a specific technology
is referred to as intention to use. Behavioral intention is a key predictor of future technology
use in technology acceptance study (Davis, 1989).
Students'
intentions
to
continue
using
AI
tools
as
supplemental
resources
for
their
learning activities may be indicative of their willingness to do so in the context of AI-supported
English learning. For instance, Sun et al. (2025) examined variables linked to college students'
intentions
to
utilize
generative
AI
chatbots
for
learning
English
and
emphasized
the
significance of motivational factors in elucidating this aim.
However, elements other than utility and usability may have an impact on intention to
use. While Acosta-Enriquez et al. (2024) uncover other elements linked with AI acceptability
in university environments, Mustofa et al. (2025) highlight the significance of trust, ethics, and
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subjective norms in AI adoption.
Therefore, a more focused understanding of students' adoption of AI technologies as
support for English language learning can be obtained by looking at intention to use in addition
to perceived usefulness and perceived ease of use.
Benefits and Challenges of Artificial Intelligence in English Learning
There are a number of possible advantages of using AI in English language instruction.
These include opportunities for language practice, instant feedback, individualized support,
and access to educational materials. Wei (2023) discovered that self-regulated learning, L2
motivation, and English learning achievement were all positively correlated with AI-mediated
instruction.
Students' experiences with these tools may also be impacted by AI literacy. Zhang et
al. (2025) discovered connections between EFL students' desire to speak, classroom anxiety,
AI literacy, and AI learning self-efficacy. These results imply that students' experiences and
inclination
to
participate
in
AI-supported
language
learning
may
be
related
to
their
comprehension and use of AI.
However, there are issues with using AI that must be taken into account. Concerns
about unequal access, privacy, and potential over-reliance on AI-generated solutions are noted
by Dugošija (2024). Almehmadi (2024) further highlights how challenging it is to find suitable
AI tools for particular educational objectives. These difficulties show that acceptability of AI
shouldn't be determined only by the accessibility of technology.
Students must also learn to think critically and assess information produced by AI in
order to use it responsibly. While Guan et al. (2025) contend that generative AI should enhance
rather than replace teacher-student interaction, Walter (2024) highlights the significance of AI
literacy and critical thinking.
These
factors
are
especially
crucial
in
university
EFL
settings,
where
AI
can
be
a
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helpful
tool
while
preserving
instructors'
and
students'
active
participation
in
the
learning
process.
Synthesis
AI is becoming more and more important in education, particularly in English language
learning,
as
the
studied
literature
shows.
Potential
advantages
of
AI
technologies
include
chances for language practice, rapid feedback, and individualized guidance, but their successful
integration
also
presents
issues
with
digital
literacy,
critical
thinking,
trust,
ethics,
and
responsible use.
A helpful theoretical foundation for comprehending how kids view these technologies
is provided by the Technology Acceptance Model. TAM is especially pertinent to the current
study since it directly addresses perceived utility, perceived ease of use, and intention to use,
even if more contemporary models like UTAUT and UTAUT2 include additional variables.
The current study examines first-level university students at the Universidad Técnica
Estatal de Quevedo's adoption of artificial intelligence tools as support for English language
acquisition by focusing on these three dimensions based on this theoretical viewpoint. The
theoretical basis for analyzing the students' answers and comprehending their opinions about
the possible application of AI tools in their English learning environment is provided by this
framework.
Methodology
The present research study adopted a quantitative approach (Risemberg et al., 2026a;
Pereira et al., 2018) as the data were collected and analyzed numerically through a structured
questionnaire and using descriptive statistics with Column Graphics, Data classes, mean and
variance values (Shitsuka et al., 2014a; Shitsuka et al., 2014b). It followed a descriptive scope,
as its purpose was to analyze students' acceptance of artificial intelligence (AI) tools as support
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3495
for English language learning
A non-experimental, cross-sectional design was employed (Risemberg et al., 2026b).
No variables were manipulated, and the information was collected at a single point in time
from the selected participants.
The population consisted of
first-level English language students at the Universidad
Técnica Estatal de Quevedo (UTEQ)
.
Eighty pupils from three parallel first-level courses made
up the sample. These categories reflected the study's accessible population and matched the
classes
given
to
two
researchers.
Consequently,
a
non-probabilistic
convenience
sampling
strategy was applied.
A total of 80 students participated in the final application of the questionnaire.
A
structured
questionnaire
created
to
examine
students'
acceptability
of
artificial
intelligence tools as a means of assisting with English language acquisition was used to gather
data.
The Technology Acceptance Model (TAM) put forward by Davis (1989) served as the
main foundation for the questionnaire's development. The tool specifically took into account
the
concepts
of
perceived
utility
and
perceived
ease
of
use,
which
are
essential
factors
in
understanding the adoption of technology.
Subsequent studies on technology acceptance and artificial intelligence in educational
settings, such as those by Venkatesh and Bala (2008), Acosta-Enriquez et al. (2024), Ibrahim
et al. (2025), Mustofa et al. (2025), and more recent research on AI acceptance in English
language learning contexts, also influenced the instrument.
The
questionnaire
comprised
a
primary
piece
with
12
topics
intended
to
gage
acceptance of AI, as well as an initial section that addressed students' use of AI tools. Three
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dimensions were used to arrange the twelve items:
Table 1.
Dimensions and Structure of the Questionnaire
|
Dimension
|
Items
|
Number of items
|
|
Perceived usefulness
|
6
–
9
|
4
|
|
Perceived ease of use
|
10
–
13
|
4
|
|
Intention to use
|
14
–
17
|
4
|
|
Total
|
6
–
17
|
12
|
Source: Research data, 2026
The items were measured using a five-point Likert scale
,
where
1 = Strongly disagree,
2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, and 5 = Strongly agree.
The Technology Acceptance Model (TAM) put forth by Davis (1989) served as the
foundation
for
the
questionnaire's
creation.
This
approach
identifies
perceived
utility
and
perceived usability as key beliefs linked to users' adoption of technology.
According to Davis (1989), perceived utility is the extent to which a person believes
that utilizing a specific technology will enhance their performance, while perceived ease of use
is the extent to which a person believes that utilizing a technology will require minimal effort.
The tool was modified to fit the unique environment of AI-assisted English language
instruction. In order to assess students' desire to employ AI tools to help their English learning,
the questionnaire includes intention to use as a third dimension in addition to the original TAM
elements.
Subsequent research on technology acceptability and recent studies looking at students'
intentions to use AI-based technologies in educational environments supported the inclusion
of intention to use (Venkatesh & Bala, 2008; Sun et al., 2025; Mustofa et al., 2025).
Therefore, rather than being a direct replication of a previously published questionnaire,
the instrument was designed based on the TAM framework and tailored to the context of AI-
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supported English language acquisition.
A pilot test was carried out with thirty students who shared characteristics with the
target group prior to the final administration. The pilot test's objectives were to assess the
questionnaire's comprehensibility and clarity, spot any potential problems with filling it out,
and confirm its broad application.
To
evaluate
the
instrument's
internal
consistency,
the
pilot
stage
responses
were
examined.
Prior
to
its
final
distribution,
the
researchers
were
able
to
examine
the
questionnaire's general functionality and language through the pilot test.
The 80 students from the three first-level parallel classrooms chosen for the study were
given the questionnaire after the pilot test.
The questionnaire was administered through an online survey to students from the three
selected parallel classes.
Before
completing
the
questionnaire,
participants
were
informed
about
the
general
purpose of the study and participated voluntarily. Once the responses were collected, the data
were organized and coded for statistical analysis.
The
responses
to
the
Likert-scale
items
were
coded
numerically
from
1
to
5,
maintaining the order of the response categories.
Microsoft Excel was used to process the gathered data. The frequencies, means, and
variances for each item and the three dimensions examined were determined using descriptive
statistics.
Cronbach's
alpha
coefficient
was
used
to
evaluate
the
questionnaire's
internal
consistency. Reliability was computed for the entire 12-item test as well as for each dimension.
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The reliability results were as follows:
Table 2.
Internal Consistency of the Questionnaire
|
Dimension
|
Items
|
Cronbach's alpha
|
|
Perceived usefulness
|
6
–
9
|
0.919
|
|
Perceived ease of use
|
10
–
13
|
0.916
|
|
Intention to use
|
14
–
17
|
0.942
|
|
Complete instrument
|
6
–
17 (12 items)
|
0.957
|
Source: Research data, 2026
The
perceived
usefulness
dimension
obtained
a
Cronbach's
alpha
of
0.919,
while
perceived ease of use obtained 0.916. The intention to use dimension presented a coefficient of
0.942.
For the complete instrument, consisting of 12 items, the Cronbach's alpha coefficient
was 0.957, indicating a high level of internal consistency among the items included in the
instrument.
Overall, the statistical analysis made possible to describe students' perceptions of AI
acceptance and to identify the relative behavior of the three dimensions examined: perceived
usefulness, perceived ease of use, and intention to use.
Results
The first part of the questionnaire examined whether students used artificial intelligence
(AI)
tools
to
support
their
English
language
learning.
The
results
obtained
from
the
80
participants are presented in Figure 1.
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3499
Figure 1. Use of AI tools for learning English.
0
10
20
30
40
50
60
70
Yes
No
Use of AI tools for learning English
Response
Source: Authors' own elaboration based on the results of the survey administered to 80 students.
Figure 1 presents the students' responses regarding their use of AI tools for learning
English. This result provides an initial description of the participants' experience with AI-
supported learning resources.
The questionnaire also examined the frequency with which students used AI tools. The
responses
were
organized
into
five
categories:
Never,
Rarely,
Sometimes,
Frequently,
and
Always
. The results are presented in Figure 2.
Figure 2. Frequency of use of AI tools.
0
5
10
15
20
25
30
35
40
45
Never
Rarely
Sometimes
Frequently
Always
Frequency of use of AI tools
.
Frequency
Source: Authors' own elaboration based on the results of the survey administered to 80 students.
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Figure 2 shows the reported frequency of students' use of AI tools. This information
provides context regarding the participants' previous experience with AI technologies before
analyzing the three dimensions of acceptance.
The acceptance of
AI tools
as support
for English
language learning was analyzed
through three dimensions: perceived usefulness, perceived ease of use, and intention to use.
Each dimension consisted of four questionnaire items.
The mean scores for the three dimensions are presented in Figure 3.
Figure 3. Mean scores of the dimensions of AI tool acceptance.
3,15
3,2
3,25
3,3
3,35
3,4
3,45
3,5
Perceived usefulness
Perceived ease of use
Intention to use
Mean scores of the dimensions of AI tool
acceptance
Dimension
Source: Authors' own elaboration based on the results of the survey administered to 80 students.
The results indicate that perceived usefulness obtained the highest mean score (M =
3.45), followed by perceived ease of use (M = 3.375) and intention to use (M = 3.26875). These
results show that students reported the most favorable perceptions regarding the usefulness of
AI tools, whereas intention to use presented the lowest mean among the three dimensions.
The first dimension, perceived usefulness, comprised Items 6
–
9. The mean and variance
obtained for each item are presented in Table 3.
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3501
Table 3.
Mean and variance of perceived usefulness items
|
Item
|
Mean
|
Variance
|
|
Item 6
|
3.55
|
0.98481013
|
|
Item 7
|
3.3875
|
0.84794304
|
|
Item 8
|
3.3875
|
1.02515823
|
|
Item 9
|
3.47
|
1,01202531
|
|
Dimension
|
3.45
|
—
|
Source: Research data, 2026
Item 6 presented the highest mean score (M = 3.55), while Items 7 and 8 obtained the
same mean (M = 3.3875). Item 9 obtained a mean of 3.47.
The second dimension, perceived ease of use, comprised Items 10
–
13. The mean and
variance obtained for each item are presented in Table 4.
Table 4.
Mean and variance of perceived ease of use items
|
Item
|
Mean
|
Variance
|
|
Item 10
|
3.4
|
0.77468354
|
|
Item 11
|
3.275
|
0.75886075
|
|
Item 12
|
3.425
|
0.72848101
|
|
Item 13
|
3.4
|
0.82531645
|
|
Dimension
|
3.375
|
—
|
Source: Research data, 2026
Item 12 obtained the highest mean score (M = 3.425), followed by Items 10 and 13 (M = 3.40). Item 11 presented
the lowest mean score (M = 3.275). The overall mean for the dimension was 3.375.
The third dimension, intention to use, comprised Items 14
–
17. The mean and variance
obtained for each item are presented in Table 5.
Table 5.
Mean and variance of intention to use items
|
Item
|
Mean
|
Variance
|
|
Item 14
|
3.2375
|
0.96819620
|
|
Item 15
|
3.225
|
0.88544303
|
|
Item 16
|
3.35
|
0.81265823
|
|
Item 17
|
3.2625
|
0.87958861
|
|
Dimension
|
3.26875
|
—
|
Source: Research data, 2026
Item 16 obtained the highest mean score (M = 3.35), while Item 15 presented the lowest mean score (M = 3.225).
Item 14 obtained a mean of 3.2375, and Item 17 obtained a mean of 3.2625. The overall mean for the dimension
was 3.26875.4.7.
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The internal consistency of the questionnaire was assessed using Cronbach's alpha. The
reliability
coefficients
obtained
for
each
dimension
and
for
the
complete
instrument
are
presented in Table 6.
Table 6.
Internal consistency of the questionnaire
|
Dimension
|
Items
|
Cronbach's Alpha
|
|
Perceived usefulness
|
6
–
9
|
0.919
|
|
Perceived ease of use
|
10
–
13
|
0.916
|
|
Intention to use
|
14
–
17
|
0.942
|
|
Complete instrument
|
6
–
17 (12 items)
|
0.957
|
Source: Research data, 2026
The complete 12-item instrument obtained a Cronbach's alpha coefficient of 0.957,
indicating
a
high
level
of
internal
consistency.
The
three
dimensions
also
presented
high
reliability coefficients: 0.919 for perceived usefulness, 0.916 for perceived ease of use, and
0.942 for intention to use.
Discussion
The
goal
of
the
current
study
was
to
examine
first-year
university
students
at
Universidad Técnica Estatal de Quevedo's adoption of artificial intelligence (AI) tools as a
means of supporting English language acquisition. With mean scores above the midpoint of
the
five-point
Likert
scale
for
each
of
the
three
dimensions
examined,
the
results
show
a
generally good degree of acceptance. Perceived usefulness obtained the highest mean (M =
3.45), followed by perceived ease of use (M = 3.375) and intention to use (M = 3.26875).
The greatest perceived usefulness score indicates that students are aware of the potential
benefits of using AI tools to aid in their English language acquisition. This result is in line with
Davis's
original
Technology
Acceptance
Model
(1989),
which
states
that
a
key
factor
influencing technology acceptance is perceived utility. Students' more positive assessment of
the utility of AI tools in the current study indicates that they are largely seen as beneficial tools
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3503
that can aid in their learning process. This result is also in line with recent studies on the
acceptability
of
AI
in
higher
education,
where
it
has
been
found
that
perceived
utility
or
performance
expectations
play
a
significant
role
in
technological
adoption.
For
instance,
Acosta-Enriquez
et
al.
(2024)
discovered
that
one
of
the
pertinent
aspects
determining
AI
acceptability in academic settings is performance expectancy.
Though marginally lower than perceived utility, the result for perceived ease of use (M
= 3.375) also shows a generally favorable view. According to this research, students generally
view
AI
tools
as
manageable
and
reasonably
accessible
for
their
educational
endeavors.
Because ease of use is a key concept that underpins technological acceptance, the outcome is
especially pertinent within the TAM framework. Davis (1989) identified perceived ease of use
as one of the two key factors that determine acceptability and showed how it relates to the
usage of technology.
This result is also consistent with Xu et al. (2024), who used the UTAUT2 model to
examine 402 university professors' acceptance of AI. Among the variables they looked at, effort
expectancy
—
which is conceptually connected to perceived ease of use
—
was found to be the
most powerful predictor of behavioral intention. Both studies stress the significance of users
viewing AI tools as controllable and reasonably simple to use, despite the differences in the
demographics and theoretical models. The favorable ease of use score for the UTEQ students
in this study may consequently influence their desire to integrate AI into English learning
activities.
However,
out
of
the
three
dimensions,
intention
to
use
had
the
lowest
mean
(M
=
3.26875). This result shows that students' willingness to utilize AI is somewhat less prominent
than their view of its usefulness and simplicity of use, even though it is still above the midpoint
of the scale. This distinction is crucial because acknowledging the utility of a technology does
not always imply a strong desire to utilize it consistently. Other variables that may influence
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intention
include
perceived
dangers,
trust,
prior
experience,
institutional
support,
and
the
suitability of the technology for particular educational tasks.
This
perspective
aligns
with
data
indicating
a
multifaceted
acceptance
of
AI
in
academic settings. Through a review of studies based on UTAUT2, Acosta-Enriquez et al.
(2024) found a number of contextual elements, such as perceived value, perceived ease of use,
and social influence, that are linked to the adoption of AI beyond utility. In a similar vein, Xu
et al. (2024) discovered that behavioral intention to utilize AI tools was linked to multiple
aspects rather than a single perception, including performance expectancy, effort expectancy,
and hedonic motivation.
The
difference
between
the
three
dimensions
is
especially
pertinent
to
the
current
study's goal. Students seem to be more aware of AI's benefits than they are of its potential
applications. This could indicate that while acceptance is growing, it has not yet solidified into
a
firm
behavioral
purpose.
Students
may
value
AI's
ability
to
offer
explanations,
practice,
feedback, or learning support in an English-learning setting, but they still need more direction
on when and how these tools should be used.
The
reliability
analysis
further
supports
the
consistency
of
the
findings.
The
three
dimensions obtained high Cronbach's alpha coefficients: 0.919 for perceived usefulness, 0.916
for perceived ease of use, and 0.942 for intention to use, while the complete 12-item instrument
obtained α = 0.957. These coefficients indicate a high level of internal consistency among the
questionnaire items and provide support for the reliability of the instrument used to examine
AI acceptance in this population.
However, the results should be interpreted in light of the particular study situation.
Since 80 first-level students from three concurrent classes at UTEQ participated in the study,
the findings only reflect the opinions of this specific group and should not be extrapolated to
all college students without more investigation. Furthermore, the descriptive approach does not
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3505
prove causal links between the variables, but it does enable the detection of patterns in students'
perceptions.
Overall,
the
results
support
the
TAM
perspective's
applicability
in
analyzing
AI
acceptance in English language learning. Students' comparatively positive opinions of AI tools'
utility and usability imply that they are seen as potentially beneficial and easily accessible
educational
aids.
However,
the
somewhat
lower
intention-to-use
score
suggests
that
when
encouraging long-term and responsible AI integration, considerations other than utility and
usability must be taken into account. In this way, the findings lend credence to the idea that
effective AI adoption in higher education necessitates not just the availability of technological
resources but also suitable pedagogical advice, digital literacy, and institutional support.
Conclusions
The
purpose
of
this
study
was
to
examine
first-level
university
students
at
the
Universidad
Técnica
Estatal
de
Quevedo's
adoption
of
artificial
intelligence
(AI)
tools
as
assistance for learning English. The results show that people typically have a positive opinion
of AI technologies, especially when it comes to their perceived usefulness, which received the
highest mean score, followed by perceived ease of use and intention to use. These findings
imply that students are aware of the potential of AI as a practical and easily available tool to
aid in their English
language
acquisition, even
though their
intention to use these tools
is
relatively less clear.
The results also confirm that the Technology Acceptance Model (TAM) is a useful tool
for analyzing how students see AI-assisted learning. The dependability of the variables used to
evaluate perceived usefulness, perceived ease of use, and intention to use is further supported
by the instrument's strong internal consistency.
The findings imply that, when used in conjunction with suitable instruction, AI tools
may play a significant role as supplemental resources in English language learning from a
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3506
pedagogical standpoint. As a result, incorporating AI into English instruction should prioritize
teaching students how to use these tools critically, responsibly, and meaningfully in addition
to giving them access to them. Lastly, the results should be evaluated in light of the study's
context, which included 80 students from three UTEQ first-level parallel classrooms.
As a result, the findings only represent the opinions of this particular group and should
not be extrapolated to the full student body. To gain a more comprehensive knowledge of AI
acceptability
in
English
language
learning,
future
research
might
look
at
bigger
and
more
varied student groups and include other elements like trust, AI literacy, institutional support,
and perceived hazards.
Lastly, the results should be evaluated in light of the study's context, which included 80
students
from
three
UTEQ
first-level
parallel
classrooms.
As
a
result,
the
findings
only
represent the opinions of this particular group and should not be extrapolated to the full student
body.
To
gain
a
more
comprehensive
knowledge
of
AI
acceptability
in
English
language
learning, future research might look at bigger and more varied student groups and include other
elements like trust, AI literacy, institutional support, and perceived hazards.
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