Vol. 12/ Núm. 4 2025 pág. 3393
https://doi.org/10.69639/arandu.v12i4.1885
Enhancing English Language Skills in Higher Education
through AI: A Systematic Review of EFL Contexts
Potenciando las habilidades del idioma inglés en la educación superior a través de la
IA: Una revisión sistemática en contextos EFL
Javier Andres Chiqui Vera
jchiquiv@unemi.edu.ec
https://orcid.org/0009-0005-6273-9518
Universidad Estatal de Milagro
Milagro-Ecuador
Estefania Nayeli Barragan Mejía
ebarraganm2@unemi.edu.ec
https://orcid.org/0000-0002-7386-1835
Universidad Estatal de Milagro
Santo Domingo – Ecuador
Jorge Francisco Zambrano Pachay
jzambranop10@unemi.edu.ec
https://orcid.org/0000-0001-9456-2765
Facultad de Posgrado, Universidad Estatal de Milagro
Milagro- Ecuador
Roxana Noemí Guapacasa Reyes
rguapacasar@unemi.edu.ec
https://orcid.org/0009-0004-9070-450X
Universidad Estatal de Milagro
La Troncal-Ecuador
Jonathan Kevin Acosta Barreno
jacostab@unemi.edu.ec
https://orcid.org/0000-0002-7062-5773
Universidad Estatal de Milagro
Milagro-Ecuador
Artículo recibido: 10 noviembre 2025 -Aceptado para publicación: 18 diciembre 2025
Conflictos de intereses: Ninguno que declarar.
ABSTRACT
This study analyzes the integration of Artificial Intelligence (AI) tools in English as a Foreign
Language (EFL) teaching within higher education contexts from 2020 to 2025. A systematic
literature review was conducted following the PRISMA 2020 protocol. Data were retrieved from
the Scopus database, resulting in the selection of 26 empirical studies that met strict inclusion
criteria regarding currency, peer review, and pedagogical application. The synthesis reveals a
predominance of Generative AI (e.g., ChatGPT) and Automated Writing Evaluation systems
(e.g., Grammarly). Findings indicate significant improvements in linguistic competence,
particularly in speaking fluency and writing accuracy, alongside positive affective outcomes such
Vol. 12/ Núm. 4 2025 pág. 3394
as reduced anxiety and increased engagement. However, a paradox of autonomy was identified,
highlighting the risk of cognitive offloading where learners may over-rely on AI assistance. The
study concludes that AI represents a fundamental shift in pedagogy rather than a mere
technological trend. To ensure effectiveness, its implementation requires an approach that
emphasizes active teacher mediation, focusing on AI literacy, critical thinking, and process-
oriented assessment to foster genuine language acquisition.
Keywords: artificial intelligence, English as a foreign language, higher education,
language skills, motivation
RESUMEN
Este estudio analiza la integración de herramientas de Inteligencia Artificial (IA) en la enseñanza
del inglés como Lengua Extranjera (ILE) en contextos de educación superior entre 2020 y 2025.:
Se realizó una revisión sistemática de la literatura siguiendo el protocolo PRISMA 2020. Los
datos fueron recuperados de la base de datos Scopus, seleccionando 26 estudios empíricos que
cumplieron con estrictos criterios de inclusión sobre actualidad, revisión por pares y aplicación
pedagógica. La síntesis revela un predominio de la IA generativa (p. ej., ChatGPT) y sistemas de
evaluación automatizada de escritura (p. ej., Grammarly). Los hallazgos indican mejoras
significativas en la competencia lingüística, particularmente en la fluidez oral y la precisión
escrita, junto con resultados afectivos positivos como la reducción de la ansiedad y un mayor
compromiso. Sin embargo, se identificó una paradoja de autonomía, resaltando el riesgo de
descarga cognitiva donde los estudiantes pueden depender excesivamente de la asistencia de la
IA. El estudio concluye que la IA representa un cambio pedagógico fundamental más que una
mera tendencia tecnológica. Para garantizar su efectividad, su implementación requiere un
enfoque de mediación docente activa, enfatizando la alfabetización en IA, el pensamiento crítico
y una evaluación orientada al proceso para fomentar una adquisición genuina del idioma.
Palabras clave: inteligencia artificial, inglés como lengua extranjera, habilidades
lingüísticas, educación superior, motivación
Todo el contenido de la Revista Científica Internacional Arandu UTIC publicado en este sitio está disponible bajo
licencia Creative Commons Atribution 4.0 International.
as reduced anxiety and increased engagement. However, a paradox of autonomy was identified,
highlighting the risk of cognitive offloading where learners may over-rely on AI assistance. The
study concludes that AI represents a fundamental shift in pedagogy rather than a mere
technological trend. To ensure effectiveness, its implementation requires an approach that
emphasizes active teacher mediation, focusing on AI literacy, critical thinking, and process-
oriented assessment to foster genuine language acquisition.
Keywords: artificial intelligence, English as a foreign language, higher education,
language skills, motivation
RESUMEN
Este estudio analiza la integración de herramientas de Inteligencia Artificial (IA) en la enseñanza
del inglés como Lengua Extranjera (ILE) en contextos de educación superior entre 2020 y 2025.:
Se realizó una revisión sistemática de la literatura siguiendo el protocolo PRISMA 2020. Los
datos fueron recuperados de la base de datos Scopus, seleccionando 26 estudios empíricos que
cumplieron con estrictos criterios de inclusión sobre actualidad, revisión por pares y aplicación
pedagógica. La síntesis revela un predominio de la IA generativa (p. ej., ChatGPT) y sistemas de
evaluación automatizada de escritura (p. ej., Grammarly). Los hallazgos indican mejoras
significativas en la competencia lingüística, particularmente en la fluidez oral y la precisión
escrita, junto con resultados afectivos positivos como la reducción de la ansiedad y un mayor
compromiso. Sin embargo, se identificó una paradoja de autonomía, resaltando el riesgo de
descarga cognitiva donde los estudiantes pueden depender excesivamente de la asistencia de la
IA. El estudio concluye que la IA representa un cambio pedagógico fundamental más que una
mera tendencia tecnológica. Para garantizar su efectividad, su implementación requiere un
enfoque de mediación docente activa, enfatizando la alfabetización en IA, el pensamiento crítico
y una evaluación orientada al proceso para fomentar una adquisición genuina del idioma.
Palabras clave: inteligencia artificial, inglés como lengua extranjera, habilidades
lingüísticas, educación superior, motivación
Todo el contenido de la Revista Científica Internacional Arandu UTIC publicado en este sitio está disponible bajo
licencia Creative Commons Atribution 4.0 International.
Vol. 12/ Núm. 4 2025 pág. 3395
INTRODUCTION
The rapid development of artificial intelligence (AI) has deeply reshaped numerous sectors
of modern society, including education. In recent years, AI has evolved from a solely
technological advancement to become a pedagogical ally able to transform teaching and learning
processes. In the area of English as a Foreign Language (ELF), the integration of AI marks a
significant step toward modernizing teaching practices, as it enables the creation of adaptive,
interactive, and student-centered approaches. Zawacki-Richter et al. (2019) point out that artificial
intelligence is no longer just a technological breakthrough, but has evolved into an essential tool
in education. It allows for the creation of more interactive and personalized learning experiences,
which are better aligned with the individual needs and paces of students.
In the context of English as a Foreign Language (EFL) teaching, these AI tools help tailor
the learning process to each student, offering a modern approach that enhances teaching practices
and better meets the demands of today’s learners. AI-based applications such as intelligent
tutoring systems, automated writing assessment, conversational agents, and speech recognition
technologies have shown their potential to customize learning experiences, provide instant
feedback, and encourage learner autonomy. AI-based tools have led to significant improvements
in reading comprehension, oral expression, vocabulary, and integrated language skills, in many
cases surpassing traditional methods (Kundu & Bej, 2025). Although research on AI in English
language teaching is still in its early stages, the growing interest in this area highlights the need
to keep exploring how teachers actually use these tools. Because teachers play a central role in
the classroom, their perceptions and attitudes greatly influence whether AI technologies can be
successfully implemented (Üretmen Karoğlu & Doğan, 2025).
Currently, the use of artificial intelligence (AI) in teaching English as a foreign language
(EFL) has established itself as a global and emerging trend in education. Internationally, many
studies conclude that the use of AI-based tools such as intelligent tutoring systems, natural
language processing, and interactive environments has led to significant improvements in reading
comprehension, oral expression, vocabulary, and integrated language skills, in many cases
surpassing traditional methods (Kundu & Bej, 2025).
Furthermore, although AI tools such as chatbots, automated writing assessment, and speech
recognition software are increasingly being used, there is still a need for empirical research on
their actual impact on language skills development, motivation, and classroom interaction. AI
integration in EFL classrooms shows both potential and risks, as it can support learning in areas
such as grammar and speaking, but also raises challenges related to teachers’ roles, pedagogical
design, and the authenticity of language use (Sumakul, Hamied, & Sukyadi, 2022).
Thus, examining the role of AI in EFL contexts is essential, particularly concerning its
potential to contribute to the development of the four language skills. Various AI tools are now
INTRODUCTION
The rapid development of artificial intelligence (AI) has deeply reshaped numerous sectors
of modern society, including education. In recent years, AI has evolved from a solely
technological advancement to become a pedagogical ally able to transform teaching and learning
processes. In the area of English as a Foreign Language (ELF), the integration of AI marks a
significant step toward modernizing teaching practices, as it enables the creation of adaptive,
interactive, and student-centered approaches. Zawacki-Richter et al. (2019) point out that artificial
intelligence is no longer just a technological breakthrough, but has evolved into an essential tool
in education. It allows for the creation of more interactive and personalized learning experiences,
which are better aligned with the individual needs and paces of students.
In the context of English as a Foreign Language (EFL) teaching, these AI tools help tailor
the learning process to each student, offering a modern approach that enhances teaching practices
and better meets the demands of today’s learners. AI-based applications such as intelligent
tutoring systems, automated writing assessment, conversational agents, and speech recognition
technologies have shown their potential to customize learning experiences, provide instant
feedback, and encourage learner autonomy. AI-based tools have led to significant improvements
in reading comprehension, oral expression, vocabulary, and integrated language skills, in many
cases surpassing traditional methods (Kundu & Bej, 2025). Although research on AI in English
language teaching is still in its early stages, the growing interest in this area highlights the need
to keep exploring how teachers actually use these tools. Because teachers play a central role in
the classroom, their perceptions and attitudes greatly influence whether AI technologies can be
successfully implemented (Üretmen Karoğlu & Doğan, 2025).
Currently, the use of artificial intelligence (AI) in teaching English as a foreign language
(EFL) has established itself as a global and emerging trend in education. Internationally, many
studies conclude that the use of AI-based tools such as intelligent tutoring systems, natural
language processing, and interactive environments has led to significant improvements in reading
comprehension, oral expression, vocabulary, and integrated language skills, in many cases
surpassing traditional methods (Kundu & Bej, 2025).
Furthermore, although AI tools such as chatbots, automated writing assessment, and speech
recognition software are increasingly being used, there is still a need for empirical research on
their actual impact on language skills development, motivation, and classroom interaction. AI
integration in EFL classrooms shows both potential and risks, as it can support learning in areas
such as grammar and speaking, but also raises challenges related to teachers’ roles, pedagogical
design, and the authenticity of language use (Sumakul, Hamied, & Sukyadi, 2022).
Thus, examining the role of AI in EFL contexts is essential, particularly concerning its
potential to contribute to the development of the four language skills. Various AI tools are now
Vol. 12/ Núm. 4 2025 pág. 3396
used to support listening comprehension, speaking, reading comprehension, and writing. Jiang
(2022) points out that artificial intelligence has strengthened EFL teaching and learning in six
major ways, including automated writing evaluation, conversational chatbots, speech recognition
tools, intelligent tutoring systems, adaptive learning platforms, and data-driven learning analytics.
In this sense, addressing the topic is relevant because it allows not only to understand the
current state of research, but also to identify strengths, limitations, and opportunities for
improvement in the integration of AI in the EFL classroom. A well-founded systematic review
will contribute to guiding both teachers and researchers in the responsible and effective
implementation of these technologies, providing evidence for pedagogical decision-making and
the design of future lines of research in language education.
Within this framework, this study seeks to analyze and synthesize recent scientific literature
(2020–2025) that explores how artificial intelligence (AI) tools and techniques are being used to
enhance English language learning in EFL contexts. Following the PRISMA 2020 protocol (Page
et al., 2021), the review aims to identify current trends in the integration of AI within English
teaching, the types of tools most commonly applied, and the language skills they tend to develop.
It also examines the pedagogical benefits, limitations, and challenges described in recent research.
Ultimately, this study aspires to build a clear and organized understanding of how AI is shaping
English language education, providing a foundation for future research, inclusive.
Theoretical framework
Generative AI
Generative Artificial Intelligence, particularly in its recent developments such as GPT-4
and GPT-4o, refers to advanced computational systems capable of producing human-like text and
generating multimodal outputs, including images and voice, through large-scale language
modelling. These models integrate sophisticated architectures and extensive training data to
generate coherent and contextually appropriate responses, which expands their potential
applications across educational, professional, and research contexts.
Lo et al. (2024) explain that Generative AI tools like ChatGPT are increasingly shaping
EFL education due to their ability to generate human-like language and provide personalised
support, although concerns persist regarding accuracy, privacy, and academic integrity. Existing
studies focus mainly on writing, leaving significant gaps in understanding their impact on other
skills. As multimodal models such as GPT-4 and GPT-4o advance, their potential in language
learning expands, but their effectiveness ultimately depends on careful, ethical, and well-
structured pedagogical use.
Adaptive AI
Delgado et al. (2020) state that AI-powered adaptive learning tools “offer the possibility
of personalizing the student’s journey with unique feedback to each online interaction” (p. 3). In
practical terms, this means that adaptive AI does far more than handle routine tasks. It observes
used to support listening comprehension, speaking, reading comprehension, and writing. Jiang
(2022) points out that artificial intelligence has strengthened EFL teaching and learning in six
major ways, including automated writing evaluation, conversational chatbots, speech recognition
tools, intelligent tutoring systems, adaptive learning platforms, and data-driven learning analytics.
In this sense, addressing the topic is relevant because it allows not only to understand the
current state of research, but also to identify strengths, limitations, and opportunities for
improvement in the integration of AI in the EFL classroom. A well-founded systematic review
will contribute to guiding both teachers and researchers in the responsible and effective
implementation of these technologies, providing evidence for pedagogical decision-making and
the design of future lines of research in language education.
Within this framework, this study seeks to analyze and synthesize recent scientific literature
(2020–2025) that explores how artificial intelligence (AI) tools and techniques are being used to
enhance English language learning in EFL contexts. Following the PRISMA 2020 protocol (Page
et al., 2021), the review aims to identify current trends in the integration of AI within English
teaching, the types of tools most commonly applied, and the language skills they tend to develop.
It also examines the pedagogical benefits, limitations, and challenges described in recent research.
Ultimately, this study aspires to build a clear and organized understanding of how AI is shaping
English language education, providing a foundation for future research, inclusive.
Theoretical framework
Generative AI
Generative Artificial Intelligence, particularly in its recent developments such as GPT-4
and GPT-4o, refers to advanced computational systems capable of producing human-like text and
generating multimodal outputs, including images and voice, through large-scale language
modelling. These models integrate sophisticated architectures and extensive training data to
generate coherent and contextually appropriate responses, which expands their potential
applications across educational, professional, and research contexts.
Lo et al. (2024) explain that Generative AI tools like ChatGPT are increasingly shaping
EFL education due to their ability to generate human-like language and provide personalised
support, although concerns persist regarding accuracy, privacy, and academic integrity. Existing
studies focus mainly on writing, leaving significant gaps in understanding their impact on other
skills. As multimodal models such as GPT-4 and GPT-4o advance, their potential in language
learning expands, but their effectiveness ultimately depends on careful, ethical, and well-
structured pedagogical use.
Adaptive AI
Delgado et al. (2020) state that AI-powered adaptive learning tools “offer the possibility
of personalizing the student’s journey with unique feedback to each online interaction” (p. 3). In
practical terms, this means that adaptive AI does far more than handle routine tasks. It observes
Vol. 12/ Núm. 4 2025 pág. 3397
how students work, responds to their progress, and adjusts instruction as they move through
different activities. By tailoring the level of challenge, the type of tasks, and the feedback they
receive, the technology acts as a supportive learning companion rather than a simple automated
program. From a pedagogical perspective, this approach strengthens inclusion, helps identify
learning gaps with greater clarity, and encourages students to take a more active and independent
role in their own learning. In EFL settings, where attending to diverse needs can be demanding,
adaptive AI offers a concrete way to create personalized learning paths that sustain engagement
and promote steady, meaningful language development at each student’s pace.
Conversational Chatbots
According to Guillermo Morales and Carcausto Calla (2025), chatbots can be understood
as AI-powered tools that enrich academic interaction by providing ongoing and personalised
practice that strengthens learners’ linguistic skills (p. 5). Rather than simply producing automated
responses, these systems operate as conversational partners that adapt to each learner’s pace,
needs, and proficiency level. This adaptability creates more opportunities for meaningful
engagement, which are often limited in traditional EFL classrooms. From this perspective,
chatbots serve as pedagogical mediators that broaden students’ exposure to the target language,
deliver immediate feedback, and foster greater learner autonomy. Because of these qualities, they
have become valuable resources for supporting language acquisition in face-to-face, hybrid, and
online learning environments.
Automated Writing Evaluation AWE
According to Wei, Wang, and Dong (2023), automated writing evaluation (AWE) refers
to AI-based systems that rely on natural language processing to analyse written texts and provide
feedback on grammar, vocabulary use, coherence, and overall organization (p. 2). This
perspective highlights that AWE tools extend far beyond identifying surface-level errors; they
function as sophisticated evaluative systems capable of examining multiple dimensions of writing
quality. Pedagogically, this means that learners can receive immediate and personalised feedback,
something that is often difficult for teachers to deliver consistently in EFL settings. By detecting
recurring patterns in students’ writing, AWE helps learners develop greater grammatical
accuracy, refine their vocabulary choices, and strengthen the flow of their ideas. As a result, these
systems offer meaningful support for writing development, complementing teacher feedback
while fostering a more independent and iterative writing process.
MATERIALS AND METHODOLOGY
This study adopts a qualitative, exploratory approach to the most current literature
regarding AI applications in English as a Foreign Language (EFL) contexts within higher
education. The objective is to explore the impact of implementing AI-based tools and on the
how students work, responds to their progress, and adjusts instruction as they move through
different activities. By tailoring the level of challenge, the type of tasks, and the feedback they
receive, the technology acts as a supportive learning companion rather than a simple automated
program. From a pedagogical perspective, this approach strengthens inclusion, helps identify
learning gaps with greater clarity, and encourages students to take a more active and independent
role in their own learning. In EFL settings, where attending to diverse needs can be demanding,
adaptive AI offers a concrete way to create personalized learning paths that sustain engagement
and promote steady, meaningful language development at each student’s pace.
Conversational Chatbots
According to Guillermo Morales and Carcausto Calla (2025), chatbots can be understood
as AI-powered tools that enrich academic interaction by providing ongoing and personalised
practice that strengthens learners’ linguistic skills (p. 5). Rather than simply producing automated
responses, these systems operate as conversational partners that adapt to each learner’s pace,
needs, and proficiency level. This adaptability creates more opportunities for meaningful
engagement, which are often limited in traditional EFL classrooms. From this perspective,
chatbots serve as pedagogical mediators that broaden students’ exposure to the target language,
deliver immediate feedback, and foster greater learner autonomy. Because of these qualities, they
have become valuable resources for supporting language acquisition in face-to-face, hybrid, and
online learning environments.
Automated Writing Evaluation AWE
According to Wei, Wang, and Dong (2023), automated writing evaluation (AWE) refers
to AI-based systems that rely on natural language processing to analyse written texts and provide
feedback on grammar, vocabulary use, coherence, and overall organization (p. 2). This
perspective highlights that AWE tools extend far beyond identifying surface-level errors; they
function as sophisticated evaluative systems capable of examining multiple dimensions of writing
quality. Pedagogically, this means that learners can receive immediate and personalised feedback,
something that is often difficult for teachers to deliver consistently in EFL settings. By detecting
recurring patterns in students’ writing, AWE helps learners develop greater grammatical
accuracy, refine their vocabulary choices, and strengthen the flow of their ideas. As a result, these
systems offer meaningful support for writing development, complementing teacher feedback
while fostering a more independent and iterative writing process.
MATERIALS AND METHODOLOGY
This study adopts a qualitative, exploratory approach to the most current literature
regarding AI applications in English as a Foreign Language (EFL) contexts within higher
education. The objective is to explore the impact of implementing AI-based tools and on the
Vol. 12/ Núm. 4 2025 pág. 3398
development and enhancement of the four language macro-skills. Accordingly, empirical
contributions published between 2020 and 2025 were systematically examined.
This systematic literature review was conducted following the PRISMA 2020 guidelines
(Page et al., 2021), which establish a standardized protocol to ensure transparency and
comprehensiveness in the identification, selection, evaluation, and synthesis of scientific studies.
The process consisted of four key phases: identification, selection, eligibility assessment, and
inclusion.
The bibliographic search was conducted using the Scopus database, selected for its
extensive international coverage and the rigorous academic and peer-review standards required
for journal indexing. The search strategy employed a Boolean string structured around three core
conceptual clusters: (1) Artificial Intelligence tools (e.g., 'artificial intelligence', 'chatbot',
'intelligent tutoring system'), (2) the EFL context (e.g., 'English as a foreign language', 'foreign
language education'), and (3) targeted learning outcomes (e.g., 'language skills', 'communicative
competence', 'proficiency').
The query was configured to scan the Title, Abstract, and Keywords (TITLE-ABS-KEY)
fields. To ensure currency and methodological rigor, filters were applied to include only records
published after 2019 (2020–present) and strictly limited to peer-reviewed journal articles,
excluding conference proceedings and book chapters. The exact search string employed was:
Table 1
Search string
Database Search equation
Scopus
TITLE-ABS-KEY("artificial intelligence" OR AI OR chatbot* OR "intelligent
tutoring system*") AND TITLE-ABS-KEY(EFL OR "English as a foreign
language" OR "language learning" OR "foreign language education") AND
TITLE-ABS-KEY("language skills" OR "communicative competence" OR
"learner autonomy" OR "language proficiency") AND PUBYEAR > 2020 AND
DOCTYPE(ar)
Selection process
A total of 218 records were retrieved during the initial search. Subsequently, inclusion
and exclusion criteria were applied to filter out articles unrelated to the study's scope. This process
resulted in the exclusion of 192 records, leaving a final total of 26 articles that fully met the
inclusion requirements. The detailed criteria are presented below in Table 2.
development and enhancement of the four language macro-skills. Accordingly, empirical
contributions published between 2020 and 2025 were systematically examined.
This systematic literature review was conducted following the PRISMA 2020 guidelines
(Page et al., 2021), which establish a standardized protocol to ensure transparency and
comprehensiveness in the identification, selection, evaluation, and synthesis of scientific studies.
The process consisted of four key phases: identification, selection, eligibility assessment, and
inclusion.
The bibliographic search was conducted using the Scopus database, selected for its
extensive international coverage and the rigorous academic and peer-review standards required
for journal indexing. The search strategy employed a Boolean string structured around three core
conceptual clusters: (1) Artificial Intelligence tools (e.g., 'artificial intelligence', 'chatbot',
'intelligent tutoring system'), (2) the EFL context (e.g., 'English as a foreign language', 'foreign
language education'), and (3) targeted learning outcomes (e.g., 'language skills', 'communicative
competence', 'proficiency').
The query was configured to scan the Title, Abstract, and Keywords (TITLE-ABS-KEY)
fields. To ensure currency and methodological rigor, filters were applied to include only records
published after 2019 (2020–present) and strictly limited to peer-reviewed journal articles,
excluding conference proceedings and book chapters. The exact search string employed was:
Table 1
Search string
Database Search equation
Scopus
TITLE-ABS-KEY("artificial intelligence" OR AI OR chatbot* OR "intelligent
tutoring system*") AND TITLE-ABS-KEY(EFL OR "English as a foreign
language" OR "language learning" OR "foreign language education") AND
TITLE-ABS-KEY("language skills" OR "communicative competence" OR
"learner autonomy" OR "language proficiency") AND PUBYEAR > 2020 AND
DOCTYPE(ar)
Selection process
A total of 218 records were retrieved during the initial search. Subsequently, inclusion
and exclusion criteria were applied to filter out articles unrelated to the study's scope. This process
resulted in the exclusion of 192 records, leaving a final total of 26 articles that fully met the
inclusion requirements. The detailed criteria are presented below in Table 2.
Vol. 12/ Núm. 4 2025 pág. 3399
Table 2
Inclusion and Exclusion criteria
Inclusion Exclusion
Journal papers published between 2020 and
2025
Conference proceedings
Peer-reviewed journal papers Technologies not involving AI
Primary research Review articles, theoretical studies without
practical application.
English as a Foreign Language setting Paper written in other languages
Intervention with tertiary education students Studies with no full-text availability (No
Open Access).
Uses AI tools or platforms in English
learning/teaching
Studies not involving the EFL context
(Teaching other languages or English in non-
EFL settings)
Involves the development of at least one of the
four core skills
Journal papers written in English
Applying these criteria resulted in the selection of 26 articles suitable for analysis, as
detailed in Table 3. A data extraction matrix was designed covering the following variables:
authors and publication year, type of AI tool applied, targeted language skill, and main findings.
Furthermore, a qualitative thematic synthesis approach was adopted for the analysis. This
process aimed to gain an in-depth understanding of the nature, characteristics and impact of AI
tools integrated into EFL teaching and learning processes in higher education. Consequently, the
analysis focused on identifying the types of artificial intelligence and the skills addressed and
interpreting the educational implications reported in the selected literature.
Figure 1 illustrates the PRISMA flow diagram, detailing the selection process and the
application of criteria used to identify studies relevant to the research objective.
Table 2
Inclusion and Exclusion criteria
Inclusion Exclusion
Journal papers published between 2020 and
2025
Conference proceedings
Peer-reviewed journal papers Technologies not involving AI
Primary research Review articles, theoretical studies without
practical application.
English as a Foreign Language setting Paper written in other languages
Intervention with tertiary education students Studies with no full-text availability (No
Open Access).
Uses AI tools or platforms in English
learning/teaching
Studies not involving the EFL context
(Teaching other languages or English in non-
EFL settings)
Involves the development of at least one of the
four core skills
Journal papers written in English
Applying these criteria resulted in the selection of 26 articles suitable for analysis, as
detailed in Table 3. A data extraction matrix was designed covering the following variables:
authors and publication year, type of AI tool applied, targeted language skill, and main findings.
Furthermore, a qualitative thematic synthesis approach was adopted for the analysis. This
process aimed to gain an in-depth understanding of the nature, characteristics and impact of AI
tools integrated into EFL teaching and learning processes in higher education. Consequently, the
analysis focused on identifying the types of artificial intelligence and the skills addressed and
interpreting the educational implications reported in the selected literature.
Figure 1 illustrates the PRISMA flow diagram, detailing the selection process and the
application of criteria used to identify studies relevant to the research objective.
Vol. 12/ Núm. 4 2025 pág. 3400
Figure 1
Prisma flow diagram
RESULTS
This systematic literature review is based on 26 empirical studies published between 2020
and 2025 that investigated the use of artificial intelligence in English as a Foreign Language (EFL)
learning within higher education settings, including colleges and universities. Only studies that
fulfilled the established inclusion criteria were incorporated into the analysis.
Table 3 summarises the core features of the selected studies, detailing the authors and
year of publication, the AI tools and systems employed, the language skills addressed, and the
principal outcomes reported. The table is intended to provide an organised overview of the
evidence rather than a complete interpretation. The analysis presented in the following sections
builds on this overview by examining shared tendencies and recurring findings across the studies.
Figure 1
Prisma flow diagram
RESULTS
This systematic literature review is based on 26 empirical studies published between 2020
and 2025 that investigated the use of artificial intelligence in English as a Foreign Language (EFL)
learning within higher education settings, including colleges and universities. Only studies that
fulfilled the established inclusion criteria were incorporated into the analysis.
Table 3 summarises the core features of the selected studies, detailing the authors and
year of publication, the AI tools and systems employed, the language skills addressed, and the
principal outcomes reported. The table is intended to provide an organised overview of the
evidence rather than a complete interpretation. The analysis presented in the following sections
builds on this overview by examining shared tendencies and recurring findings across the studies.
Vol. 12/ Núm. 4 2025 pág. 3401
Table 3
Data extraction matrix
Author(s) &
year
Design and
sample
AI tool /
platform
AI
category
Skill(s)
addressed
Main
outcomes
Zakarneh et al.
(2025)
Quantitative
study (survey-
based)/ 398
undergraduate
English
students
ChatGPT
Generativ
e AI
chatbot
Speaking,
writing,
vocabulary,
grammar,
reading
Improved
perceived
language
development,
motivation,
and
autonomy
Xodabande et al.
(2025)
Randomized
Controlled Trial
(RCT)/60
intermediate
EFL learners
ChatGPT
Generativ
e AI
chatbot
Pronunciati
on
(speaking)
Significant
gains in
pronunciation
accuracy and
retention
Jalambo et al.
(2025)
Quasi-
experimental
design /187 EFL
learners (93
Control, 94
Experimental)
AI chatbot
Generativ
e AI
chatbot
Vocabulary,
collocations
Improved
vocabulary
learning,
reduced
boredom,
higher
autonomy
Zheldibayeva
(2025)
Quasi-
experimental
design/ 93
undergraduate
students (48
Exp, 45 Comp)
ChatGPT,
Gemini
Generativ
e AI
chatbot
Listening,
writing
Significant
improvement
in listening
and writing
performance
Duong &
Suppasetseree
(2024)
Quasi-
experimental
design (8
weeks) /30
undergraduate
students
AI voice
chatbot
Generativ
e AI
chatbot
Speaking
Improved
fluency,
accuracy, and
confidence
Polakova &
Klimova (2024)
Pilot
experimental
study /58
university
students (B2
and C1 levels)
ChatGPT
Generativ
e AI
chatbot
Writing,
grammar,
vocabulary
Positive
perceptions
and gains in
language
accuracy
Hajihasankhansa
ry & Gilanlioglu
(2025)
Exploratory
sequential
mixed-methods
design /107
graduate
students
AI-
generated
corpus
Intelligen
t AI
system
Grammar,
lexical
bundles
Significant
gains and
increased
willingness to
write
Table 3
Data extraction matrix
Author(s) &
year
Design and
sample
AI tool /
platform
AI
category
Skill(s)
addressed
Main
outcomes
Zakarneh et al.
(2025)
Quantitative
study (survey-
based)/ 398
undergraduate
English
students
ChatGPT
Generativ
e AI
chatbot
Speaking,
writing,
vocabulary,
grammar,
reading
Improved
perceived
language
development,
motivation,
and
autonomy
Xodabande et al.
(2025)
Randomized
Controlled Trial
(RCT)/60
intermediate
EFL learners
ChatGPT
Generativ
e AI
chatbot
Pronunciati
on
(speaking)
Significant
gains in
pronunciation
accuracy and
retention
Jalambo et al.
(2025)
Quasi-
experimental
design /187 EFL
learners (93
Control, 94
Experimental)
AI chatbot
Generativ
e AI
chatbot
Vocabulary,
collocations
Improved
vocabulary
learning,
reduced
boredom,
higher
autonomy
Zheldibayeva
(2025)
Quasi-
experimental
design/ 93
undergraduate
students (48
Exp, 45 Comp)
ChatGPT,
Gemini
Generativ
e AI
chatbot
Listening,
writing
Significant
improvement
in listening
and writing
performance
Duong &
Suppasetseree
(2024)
Quasi-
experimental
design (8
weeks) /30
undergraduate
students
AI voice
chatbot
Generativ
e AI
chatbot
Speaking
Improved
fluency,
accuracy, and
confidence
Polakova &
Klimova (2024)
Pilot
experimental
study /58
university
students (B2
and C1 levels)
ChatGPT
Generativ
e AI
chatbot
Writing,
grammar,
vocabulary
Positive
perceptions
and gains in
language
accuracy
Hajihasankhansa
ry & Gilanlioglu
(2025)
Exploratory
sequential
mixed-methods
design /107
graduate
students
AI-
generated
corpus
Intelligen
t AI
system
Grammar,
lexical
bundles
Significant
gains and
increased
willingness to
write
Vol. 12/ Núm. 4 2025 pág. 3402
Lu (2025)
Quasi-
experimental
study (repeated
measures)/80
EFL students
AI-
generated
corpus
Intelligen
t AI
system
Grammar,
vocabulary
Sustained
improvement
s and higher
engagement
Wangdi &
Shimray (2025)
Mixed-methods
research /54
EFL
undergraduate
students
ReadTheor
y
Adaptive
AI
platform
Reading
Improved
comprehensi
on and
reading
enjoyment
Liu (2025)
Empirical
experiment /262
learners
AI-
enhanced
learning
system
Intelligen
t AI
system
Listening,
speaking,
reading,
writing
Large gains
across all
skills and
intercultural
competence
Ma & Chen
(2025)
Longitudinal
quasi-
experimental
mixed-
methods150
intermediate E
FL learners
LinguaQue
st AI
Adaptive
AI
platform
Integrated
skills
Strongest
gains when
combined
with teacher
scaffolding
Qiao & Zhao
(2023)
Experimental
design /93 EFL
learners
Duolingo
Adaptive
AI
applicatio
n
Speaking
Improved
speaking
performance
and self-
regulation
Phanwiriyarat et
al. (2025)
Mixed-methods
design48 first-
year
undergraduate
students
Duolingo
Adaptive
AI
applicatio
n
Speaking
Improved
oral
performance
and
confidence
Asmar et al.
(2025)
Exploratory
mixed-methods
case study/189
students
Duolingo
Adaptive
AI
applicatio
n
Integrated
skills
Higher
engagement
and perceived
skill
improvement
Khlaisang &
Sukavatee
(2024)
Mixed-methods
(Quant. &
Qual.)546
higher
education
learners
MALLIE
chatbot
system
Adaptive
AI
applicatio
n
Integrated
skills
Enhanced
communicati
on skills
Zhou et al.
(2025)
Quasi-
experimental
mixed-methods
ChatGPT-4
Generativ
e AI
chatbot
Listening
Large gains in
listening
comprehensi
on
Lu (2025)
Quasi-
experimental
study (repeated
measures)/80
EFL students
AI-
generated
corpus
Intelligen
t AI
system
Grammar,
vocabulary
Sustained
improvement
s and higher
engagement
Wangdi &
Shimray (2025)
Mixed-methods
research /54
EFL
undergraduate
students
ReadTheor
y
Adaptive
AI
platform
Reading
Improved
comprehensi
on and
reading
enjoyment
Liu (2025)
Empirical
experiment /262
learners
AI-
enhanced
learning
system
Intelligen
t AI
system
Listening,
speaking,
reading,
writing
Large gains
across all
skills and
intercultural
competence
Ma & Chen
(2025)
Longitudinal
quasi-
experimental
mixed-
methods150
intermediate E
FL learners
LinguaQue
st AI
Adaptive
AI
platform
Integrated
skills
Strongest
gains when
combined
with teacher
scaffolding
Qiao & Zhao
(2023)
Experimental
design /93 EFL
learners
Duolingo
Adaptive
AI
applicatio
n
Speaking
Improved
speaking
performance
and self-
regulation
Phanwiriyarat et
al. (2025)
Mixed-methods
design48 first-
year
undergraduate
students
Duolingo
Adaptive
AI
applicatio
n
Speaking
Improved
oral
performance
and
confidence
Asmar et al.
(2025)
Exploratory
mixed-methods
case study/189
students
Duolingo
Adaptive
AI
applicatio
n
Integrated
skills
Higher
engagement
and perceived
skill
improvement
Khlaisang &
Sukavatee
(2024)
Mixed-methods
(Quant. &
Qual.)546
higher
education
learners
MALLIE
chatbot
system
Adaptive
AI
applicatio
n
Integrated
skills
Enhanced
communicati
on skills
Zhou et al.
(2025)
Quasi-
experimental
mixed-methods
ChatGPT-4
Generativ
e AI
chatbot
Listening
Large gains in
listening
comprehensi
on
Vol. 12/ Núm. 4 2025 pág. 3403
study/ 67
students
Dizon & Gold
(2023)
Quasi-
experimental
design /58 EFL
students in
academic
writing courses
Grammarly AWE
Writing
(affective
focus)
Reduced
writing
anxiety
Murtisari et al.
(2025)
Mixed-method
multiple case
study
Grammarly AWE Writing
Effects
mediated by
proficiency
level
Shen et al.
(2023)
Mixed-methods
(Process &
product-based)/
42 EFL learners
Pigai AWE Writing
Differential
gains in
accuracy and
lexical
complexity
Xu & Jumaat
(2024)
Mixed-methods
approach/ 60
university
juniors
ChatGPT
Generativ
e AI
(writing
support)
Academic
writing
Improved
writing
strategies and
confidence
Robillos (2024)
Sequential
mixed-methods
design /30
university EFL
students
GPT
chatbot
Generativ
e AI
(writing
support)
Writing
Improved
writing
quality and
reflection
Moussa &
Belhiah (2024)
Quasi-
experimental
study /62
students
AI-assisted
writing
tools
AWE /
GenAI Writing
Improved
linguistic
competence
and creativity
Sayed et al.
(2024)
Concurrent
mixed-methods
design /28
upper-
intermediate
EFL learners
AI-
supported
oral testing
AI-
supported
assessme
nt
Speaking
Improved
speaking,
autonomy,
academic
buoyancy
Abdellatif et al.
(2024)
Experimental
design /57 EFL
students
AI-
supported
listening
exams
AI-
supported
assessme
nt
Listening
Improved
listening
performance
and resilience
Zyouda et al.
(2023)
Qualitative case
study/ 25
undergraduate
students
Multiple
AI chatbots
Generativ
e AI
chatbot
Multiple
skills
Increased
autonomy
and perceived
competence
study/ 67
students
Dizon & Gold
(2023)
Quasi-
experimental
design /58 EFL
students in
academic
writing courses
Grammarly AWE
Writing
(affective
focus)
Reduced
writing
anxiety
Murtisari et al.
(2025)
Mixed-method
multiple case
study
Grammarly AWE Writing
Effects
mediated by
proficiency
level
Shen et al.
(2023)
Mixed-methods
(Process &
product-based)/
42 EFL learners
Pigai AWE Writing
Differential
gains in
accuracy and
lexical
complexity
Xu & Jumaat
(2024)
Mixed-methods
approach/ 60
university
juniors
ChatGPT
Generativ
e AI
(writing
support)
Academic
writing
Improved
writing
strategies and
confidence
Robillos (2024)
Sequential
mixed-methods
design /30
university EFL
students
GPT
chatbot
Generativ
e AI
(writing
support)
Writing
Improved
writing
quality and
reflection
Moussa &
Belhiah (2024)
Quasi-
experimental
study /62
students
AI-assisted
writing
tools
AWE /
GenAI Writing
Improved
linguistic
competence
and creativity
Sayed et al.
(2024)
Concurrent
mixed-methods
design /28
upper-
intermediate
EFL learners
AI-
supported
oral testing
AI-
supported
assessme
nt
Speaking
Improved
speaking,
autonomy,
academic
buoyancy
Abdellatif et al.
(2024)
Experimental
design /57 EFL
students
AI-
supported
listening
exams
AI-
supported
assessme
nt
Listening
Improved
listening
performance
and resilience
Zyouda et al.
(2023)
Qualitative case
study/ 25
undergraduate
students
Multiple
AI chatbots
Generativ
e AI
chatbot
Multiple
skills
Increased
autonomy
and perceived
competence
Vol. 12/ Núm. 4 2025 pág. 3404
Generative AI and conversational systems
The majority of the studies included in this review focused on generative artificial
intelligence, particularly conversational systems, with ChatGPT standing out as the most
frequently examined tool. In several investigations, ChatGPT was used independently as a
conversational partner, whereas other studies embedded it within structured instructional tasks or
combined it with voice-based interaction to facilitate oral practice. A smaller number of studies
also explored alternative generative tools, such as Gemini or AI-driven voice chatbots specifically
designed to support spoken interaction.
Across the reviewed literature, generative AI chatbots were mainly employed to enhance
speaking-related skills, including oral fluency, pronunciation, vocabulary development, and
listening comprehension. The findings consistently pointed to improvements in learners’ spoken
performance, especially in terms of fluency and pronunciation accuracy. Beyond linguistic gains,
many studies highlighted notable increases in learners’ motivation, confidence, and willingness
to communicate. These positive effects were often attributed to the low-anxiety nature of chatbot
interactions, which allowed students to practise repeatedly, experiment with language, and receive
immediate feedback without the pressure typically associated with classroom participation.
AI-assisted writing and automated writing evaluation systems
A considerable portion of the reviewed studies concentrated on AI-assisted writing tools
and automated writing evaluation (AWE) systems, most commonly Grammarly and Pigai. These
tools were primarily implemented in academic writing contexts, where they provided automated
feedback during drafting and revision processes, focusing on aspects such as grammatical
accuracy, vocabulary choice, coherence, and overall text organisation.
The findings across these studies suggest that the use of Grammarly and Pigai contributed
to measurable improvements in writing accuracy and overall text quality. In addition, several
studies reported reductions in writing-related anxiety, particularly among learners who perceived
automated feedback as less intimidating than teacher correction. However, the results also
revealed important differences linked to learners’ proficiency levels. Lower-proficiency learners
tended to focus mainly on surface-level corrections, while more advanced learners engaged more
critically with the feedback and used it to refine content and structure. As a result, multiple studies
emphasised the need for pedagogical guidance to ensure that AWE tools support meaningful
learning rather than encouraging mechanical error correction.
Adaptive systems and application-based AI platforms
A smaller yet relevant set of studies examined adaptive and application-based AI
platforms, with Duolingo and ReadTheory receiving the most attention. Duolingo was frequently
analysed in relation to vocabulary acquisition, speaking development, and learner engagement,
often within gamified or flipped classroom approaches. The findings generally indicated
Generative AI and conversational systems
The majority of the studies included in this review focused on generative artificial
intelligence, particularly conversational systems, with ChatGPT standing out as the most
frequently examined tool. In several investigations, ChatGPT was used independently as a
conversational partner, whereas other studies embedded it within structured instructional tasks or
combined it with voice-based interaction to facilitate oral practice. A smaller number of studies
also explored alternative generative tools, such as Gemini or AI-driven voice chatbots specifically
designed to support spoken interaction.
Across the reviewed literature, generative AI chatbots were mainly employed to enhance
speaking-related skills, including oral fluency, pronunciation, vocabulary development, and
listening comprehension. The findings consistently pointed to improvements in learners’ spoken
performance, especially in terms of fluency and pronunciation accuracy. Beyond linguistic gains,
many studies highlighted notable increases in learners’ motivation, confidence, and willingness
to communicate. These positive effects were often attributed to the low-anxiety nature of chatbot
interactions, which allowed students to practise repeatedly, experiment with language, and receive
immediate feedback without the pressure typically associated with classroom participation.
AI-assisted writing and automated writing evaluation systems
A considerable portion of the reviewed studies concentrated on AI-assisted writing tools
and automated writing evaluation (AWE) systems, most commonly Grammarly and Pigai. These
tools were primarily implemented in academic writing contexts, where they provided automated
feedback during drafting and revision processes, focusing on aspects such as grammatical
accuracy, vocabulary choice, coherence, and overall text organisation.
The findings across these studies suggest that the use of Grammarly and Pigai contributed
to measurable improvements in writing accuracy and overall text quality. In addition, several
studies reported reductions in writing-related anxiety, particularly among learners who perceived
automated feedback as less intimidating than teacher correction. However, the results also
revealed important differences linked to learners’ proficiency levels. Lower-proficiency learners
tended to focus mainly on surface-level corrections, while more advanced learners engaged more
critically with the feedback and used it to refine content and structure. As a result, multiple studies
emphasised the need for pedagogical guidance to ensure that AWE tools support meaningful
learning rather than encouraging mechanical error correction.
Adaptive systems and application-based AI platforms
A smaller yet relevant set of studies examined adaptive and application-based AI
platforms, with Duolingo and ReadTheory receiving the most attention. Duolingo was frequently
analysed in relation to vocabulary acquisition, speaking development, and learner engagement,
often within gamified or flipped classroom approaches. The findings generally indicated
Vol. 12/ Núm. 4 2025 pág. 3405
improvements in oral performance, increased confidence, and high levels of engagement,
particularly among learners at beginner and intermediate proficiency levels.
ReadTheory, an AI-driven adaptive reading platform, was associated with gains in
reading comprehension and increased learner enjoyment of reading tasks. Other mobile and web-
based platforms combined features such as chatbot interaction, speech recognition, and adaptive
feedback to support self-paced learning. While these tools demonstrated positive outcomes,
several studies noted limitations related to content depth and their suitability for more advanced
learners, suggesting that their effectiveness may vary depending on instructional goals and learner
profiles.
English language skills addressed
An examination of the targeted language skills revealed that writing was by far the most
frequently investigated area, followed by speaking and listening, whereas reading received
comparatively limited attention. Studies focusing on speaking commonly reported improvements
in fluency, pronunciation, confidence, and willingness to communicate, particularly when tools
such as ChatGPT, AI voice chatbots, or Duolingo were employed.
Writing-oriented studies, which predominantly used Grammarly, Pigai, and GPT-based
writing assistants, documented gains in grammatical accuracy, lexical precision, coherence, and
textual organisation. Listening skills were addressed mainly through generative chatbots with
audio features, AI-supported listening tasks, and adaptive platforms, with findings indicating
improvements in listening comprehension and learner confidence. Reading skills were examined
less frequently and were mostly supported through adaptive applications like ReadTheory, which
nonetheless showed positive effects on comprehension and engagement.
Affective and learner-related outcomes
Beyond language performance, a substantial number of studies reported positive effects
on affective and learner-centred variables. Increased motivation, engagement, learner autonomy,
and self-regulation were commonly linked to AI-supported learning environments. Several
studies also noted reductions in speaking anxiety, writing anxiety, and learning-related boredom,
particularly when learners benefited from immediate feedback and flexible opportunities for
independent practice.
At the same time, the literature identified several challenges. These included learners’
potential overreliance on automated feedback, differences in engagement across proficiency
levels, and concerns about the depth and accuracy of AI-generated responses. Such findings
underscore that the benefits of AI tools are closely tied to how they are integrated into instructional
practices and supported through appropriate pedagogical design.
improvements in oral performance, increased confidence, and high levels of engagement,
particularly among learners at beginner and intermediate proficiency levels.
ReadTheory, an AI-driven adaptive reading platform, was associated with gains in
reading comprehension and increased learner enjoyment of reading tasks. Other mobile and web-
based platforms combined features such as chatbot interaction, speech recognition, and adaptive
feedback to support self-paced learning. While these tools demonstrated positive outcomes,
several studies noted limitations related to content depth and their suitability for more advanced
learners, suggesting that their effectiveness may vary depending on instructional goals and learner
profiles.
English language skills addressed
An examination of the targeted language skills revealed that writing was by far the most
frequently investigated area, followed by speaking and listening, whereas reading received
comparatively limited attention. Studies focusing on speaking commonly reported improvements
in fluency, pronunciation, confidence, and willingness to communicate, particularly when tools
such as ChatGPT, AI voice chatbots, or Duolingo were employed.
Writing-oriented studies, which predominantly used Grammarly, Pigai, and GPT-based
writing assistants, documented gains in grammatical accuracy, lexical precision, coherence, and
textual organisation. Listening skills were addressed mainly through generative chatbots with
audio features, AI-supported listening tasks, and adaptive platforms, with findings indicating
improvements in listening comprehension and learner confidence. Reading skills were examined
less frequently and were mostly supported through adaptive applications like ReadTheory, which
nonetheless showed positive effects on comprehension and engagement.
Affective and learner-related outcomes
Beyond language performance, a substantial number of studies reported positive effects
on affective and learner-centred variables. Increased motivation, engagement, learner autonomy,
and self-regulation were commonly linked to AI-supported learning environments. Several
studies also noted reductions in speaking anxiety, writing anxiety, and learning-related boredom,
particularly when learners benefited from immediate feedback and flexible opportunities for
independent practice.
At the same time, the literature identified several challenges. These included learners’
potential overreliance on automated feedback, differences in engagement across proficiency
levels, and concerns about the depth and accuracy of AI-generated responses. Such findings
underscore that the benefits of AI tools are closely tied to how they are integrated into instructional
practices and supported through appropriate pedagogical design.
Vol. 12/ Núm. 4 2025 pág. 3406
DISCUSSION
The findings of this systematic review reveal a significant transformation in English as a
Foreign Language (EFL) education within higher education, driven by the integration of Artificial
Intelligence (AI). The analysis of the selected empirical studies confirms that tools such as
Generative AI (GenAI), conversational chatbots, and adaptive platforms not only enhance
linguistic competence but also redefine learner autonomy and the affective landscape of learning.
However, the evidence also underscores the critical need for pedagogical scaffolding to prevent
passive dependency and foster higher-order cognitive skills.
Enhancement of Linguistic Competence and Long-Term Retention
A recurring theme in the reviewed literature is the superior efficacy of AI tools in
fostering not just immediate performance, but also long-term skill retention compared to
traditional methods. In the domain of pronunciation, the advantage of AI-driven practice lies in
its interactivity and immediacy. While traditional tools provide static models, interfaces like
ChatGPT allow for iterative cycles of production and feedback, which are crucial for phonological
encoding (Xodabande et al., 2025). Empirical findings indicate that students who used ChatGPT
for pronunciation practice performed better than those in the control group. These learners showed
higher results immediately after the intervention, and their improvement was still evident in later
assessments, suggesting that the learning achieved was retained over time (Xodabande et al.,
2025).Similarly, the use of voice chatbots has been shown to significantly improve students'
fluency, accuracy, and confidence in oral communication (Duong & Suppasetseree, 2024), a
finding supported by systems specifically designed for this purpose, such as the MALLIE chatbot,
which enhanced communicative skills in university settings (Khlaisang & Sukavatee, 2024).
In the acquisition of lexical competence, the role of AI extends beyond simple definitions
to the mastery of complex collocations. Integrating chatbots into self-regulated learning (SRL)
strategies has been shown to significantly improve incidental vocabulary learning and receptive
knowledge of collocations (Jalambo et al., 2025). The mechanism behind this success appears to
be the high-frequency exposure and contextualized input provided by chatbots, which mimic
authentic dialogue more effectively than traditional exercises. Furthermore, the use of AI-
generated corpora has proven effective for students to acquire "lexical bundles" and grammatical
structures, outperforming instruction based solely on textbooks (Lu, 2025).
Redefining the Affective Domain: Anxiety, Boredom, and Resilience
Beyond cognitive gains, this review highlights the profound impact of AI on the affective
dimensions of learning, specifically in mitigating boredom and anxiety. Traditional repetitive
practice often leads to disengagement; however, the interactive and gamified nature of GenAI
chatbots creates a flow state that significantly reduces boredom levels among students (Jalambo
et al., 2025). Platforms like Duolingo, when implemented in higher education contexts, not only
DISCUSSION
The findings of this systematic review reveal a significant transformation in English as a
Foreign Language (EFL) education within higher education, driven by the integration of Artificial
Intelligence (AI). The analysis of the selected empirical studies confirms that tools such as
Generative AI (GenAI), conversational chatbots, and adaptive platforms not only enhance
linguistic competence but also redefine learner autonomy and the affective landscape of learning.
However, the evidence also underscores the critical need for pedagogical scaffolding to prevent
passive dependency and foster higher-order cognitive skills.
Enhancement of Linguistic Competence and Long-Term Retention
A recurring theme in the reviewed literature is the superior efficacy of AI tools in
fostering not just immediate performance, but also long-term skill retention compared to
traditional methods. In the domain of pronunciation, the advantage of AI-driven practice lies in
its interactivity and immediacy. While traditional tools provide static models, interfaces like
ChatGPT allow for iterative cycles of production and feedback, which are crucial for phonological
encoding (Xodabande et al., 2025). Empirical findings indicate that students who used ChatGPT
for pronunciation practice performed better than those in the control group. These learners showed
higher results immediately after the intervention, and their improvement was still evident in later
assessments, suggesting that the learning achieved was retained over time (Xodabande et al.,
2025).Similarly, the use of voice chatbots has been shown to significantly improve students'
fluency, accuracy, and confidence in oral communication (Duong & Suppasetseree, 2024), a
finding supported by systems specifically designed for this purpose, such as the MALLIE chatbot,
which enhanced communicative skills in university settings (Khlaisang & Sukavatee, 2024).
In the acquisition of lexical competence, the role of AI extends beyond simple definitions
to the mastery of complex collocations. Integrating chatbots into self-regulated learning (SRL)
strategies has been shown to significantly improve incidental vocabulary learning and receptive
knowledge of collocations (Jalambo et al., 2025). The mechanism behind this success appears to
be the high-frequency exposure and contextualized input provided by chatbots, which mimic
authentic dialogue more effectively than traditional exercises. Furthermore, the use of AI-
generated corpora has proven effective for students to acquire "lexical bundles" and grammatical
structures, outperforming instruction based solely on textbooks (Lu, 2025).
Redefining the Affective Domain: Anxiety, Boredom, and Resilience
Beyond cognitive gains, this review highlights the profound impact of AI on the affective
dimensions of learning, specifically in mitigating boredom and anxiety. Traditional repetitive
practice often leads to disengagement; however, the interactive and gamified nature of GenAI
chatbots creates a flow state that significantly reduces boredom levels among students (Jalambo
et al., 2025). Platforms like Duolingo, when implemented in higher education contexts, not only
Vol. 12/ Núm. 4 2025 pág. 3407
improve oral performance but also foster greater engagement and self-regulation thanks to their
gamified elements (Qiao & Zhao, 2023; Phanwiriyarat et al., 2025; Asmar et al., 2025).
Qualitative evidence reinforces that students perceive interactions with chatbots as safer
and less intimidating environments than human interaction, providing a judgment-free zone that
encourages experimentation and reduces the anxiety typically associated with making errors in
front of peers (Taeza, 2025; Moussa & Belhiah, 2024). Lowering affective barriers plays an
important role in language learning, as it encourages students to communicate more confidently
and remain engaged with the language beyond the classroom (Taeza, 2025). In addition, research
shows that when AI tools are used in oral and listening assessments, students tend to cope better
with pressure. These tools help learners manage academic difficulties more effectively and feel
less anxious during exams, especially in demanding assessment situations (Sayed et al., 2024;
Abdellatif et al., 2024).
Balancing Learner Independence with the Imperative of Critical Evaluation
While the promotion of learner autonomy is a celebrated benefit of AI integration, a
critical interpretation of the findings reveals a potential paradox: the risk of cognitive offloading
and over-reliance. Although students report that AI tools satisfy their curiosity and improve time
efficiency (Zakarneh et al., 2025), unmediated access can lead to superficial engagement where
AI replaces, rather than supports, intellectual effort. Studies on automated writing evaluation
(AWE) tools like Grammarly indicate that while they improve grammatical precision, students
with lower linguistic proficiency may accept suggestions mechanically without deep cognitive
engagement (Murtisari et al., 2025).
The literature suggests that uncritical reliance in AI outputs is a significant challenge,
particularly for graduate students who may rely on these tools to compensate for linguistic
weaknesses without critically evaluating the generated content (Hajihasankhansary &
Gilanlioglu, 2025). Consequently, the integration of Critical Thinking (CT) into language
instruction emerges not just as an option, but as a necessity in the AI era. Interventions that
explicitly combine CT instruction with language learning have proven effective in transforming
students from passive consumers of AI content into active evaluators (Hajihasankhansary &
Gilanlioglu, 2025). Furthermore, the use of chatbots can foster reflective thinking, allowing
students to improve the quality of their writing through technology-assisted critical revision of
their own drafts (Robillos, 2024).
The Role of the Teacher and the Cultural Dimension
Despite technological sophistication, the evidence reaffirms the central role of the
teacher. Comparative studies demonstrate that the use of AI platforms combined with teacher
scaffolding produces significantly higher gains in integrated skills than the use of AI in isolation
(Ma & Chen, 2025). AI can reduce cognitive load and offer immediate feedback, but it is
improve oral performance but also foster greater engagement and self-regulation thanks to their
gamified elements (Qiao & Zhao, 2023; Phanwiriyarat et al., 2025; Asmar et al., 2025).
Qualitative evidence reinforces that students perceive interactions with chatbots as safer
and less intimidating environments than human interaction, providing a judgment-free zone that
encourages experimentation and reduces the anxiety typically associated with making errors in
front of peers (Taeza, 2025; Moussa & Belhiah, 2024). Lowering affective barriers plays an
important role in language learning, as it encourages students to communicate more confidently
and remain engaged with the language beyond the classroom (Taeza, 2025). In addition, research
shows that when AI tools are used in oral and listening assessments, students tend to cope better
with pressure. These tools help learners manage academic difficulties more effectively and feel
less anxious during exams, especially in demanding assessment situations (Sayed et al., 2024;
Abdellatif et al., 2024).
Balancing Learner Independence with the Imperative of Critical Evaluation
While the promotion of learner autonomy is a celebrated benefit of AI integration, a
critical interpretation of the findings reveals a potential paradox: the risk of cognitive offloading
and over-reliance. Although students report that AI tools satisfy their curiosity and improve time
efficiency (Zakarneh et al., 2025), unmediated access can lead to superficial engagement where
AI replaces, rather than supports, intellectual effort. Studies on automated writing evaluation
(AWE) tools like Grammarly indicate that while they improve grammatical precision, students
with lower linguistic proficiency may accept suggestions mechanically without deep cognitive
engagement (Murtisari et al., 2025).
The literature suggests that uncritical reliance in AI outputs is a significant challenge,
particularly for graduate students who may rely on these tools to compensate for linguistic
weaknesses without critically evaluating the generated content (Hajihasankhansary &
Gilanlioglu, 2025). Consequently, the integration of Critical Thinking (CT) into language
instruction emerges not just as an option, but as a necessity in the AI era. Interventions that
explicitly combine CT instruction with language learning have proven effective in transforming
students from passive consumers of AI content into active evaluators (Hajihasankhansary &
Gilanlioglu, 2025). Furthermore, the use of chatbots can foster reflective thinking, allowing
students to improve the quality of their writing through technology-assisted critical revision of
their own drafts (Robillos, 2024).
The Role of the Teacher and the Cultural Dimension
Despite technological sophistication, the evidence reaffirms the central role of the
teacher. Comparative studies demonstrate that the use of AI platforms combined with teacher
scaffolding produces significantly higher gains in integrated skills than the use of AI in isolation
(Ma & Chen, 2025). AI can reduce cognitive load and offer immediate feedback, but it is
Vol. 12/ Núm. 4 2025 pág. 3408
pedagogical guidance that ensures these tools are used for meaningful learning and not just for
error correction (Ma & Chen, 2025; Shen et al., 2023).
Finally, the review indicates that the scope of AI in EFL is expanding from purely
linguistic accuracy towards intercultural communicative competence. Advanced intelligent
systems integrating deep learning with cultural context simulations can bridge the gap between
linguistic correctness and cultural appropriateness (Liu, 2025). By processing multimodal data
and providing real-time feedback on cultural nuances, these systems allow students to navigate
complex intercultural scenarios, suggesting that AI has the potential to democratize access to
immersive cultural training (Liu, 2025). Self-access platforms like ReadTheory also contribute to
this, enhancing reading enjoyment and comprehension through a posthumanist approach that
integrates technology and human agency (Wangdi & Shimray, 2025).
In summary, the integration of AI in higher education EFL contexts offers a robust
pathway to enhance linguistic skills and emotional engagement. However, its sustainable
implementation requires a pedagogical shift: moving from viewing AI as a simple shortcut for
production to treating it as a sophisticated partner that requires self-regulation (Xu & Jumaat,
2024), critical oversight, and active learner engagement to be truly effective.
CONCLUSION
The systematic analysis of the selected literature confirms that the integration of Artificial
Intelligence (AI) into higher education EFL contexts represents a fundamental pedagogical shift
rather than a mere technological trend. The evidence suggests that generative AI, conversational
chatbots, and automated writing evaluation (AWE) systems function as effective catalysts for
linguistic development, particularly in enhancing speaking fluency, pronunciation accuracy, and
writing mechanics. Beyond cognitive gains, these tools successfully address the affective
dimensions of learning by lowering anxiety, mitigating boredom through gamification, and
fostering a psychologically safe environment for practice and assessment.
However, this research is not without its limitations. First, the scope of this review was
restricted to 26 articles selected from specific academic databases, which, while rigorous, may
not capture the entirety of the rapidly expanding body of literature on AI in education. Second,
the focus was exclusively on higher education settings; therefore, the positive outcomes reported
here cannot be automatically generalized to K-12 contexts where learner autonomy and digital
literacy levels differ significantly.
Future research must prioritize longitudinal designs that extend beyond a single semester.
It is vital to determine if the linguistic gains and motivation provided by AI sustain themselves
once the novelty diminishes or if they regress, as hinted by some follow-up data.
pedagogical guidance that ensures these tools are used for meaningful learning and not just for
error correction (Ma & Chen, 2025; Shen et al., 2023).
Finally, the review indicates that the scope of AI in EFL is expanding from purely
linguistic accuracy towards intercultural communicative competence. Advanced intelligent
systems integrating deep learning with cultural context simulations can bridge the gap between
linguistic correctness and cultural appropriateness (Liu, 2025). By processing multimodal data
and providing real-time feedback on cultural nuances, these systems allow students to navigate
complex intercultural scenarios, suggesting that AI has the potential to democratize access to
immersive cultural training (Liu, 2025). Self-access platforms like ReadTheory also contribute to
this, enhancing reading enjoyment and comprehension through a posthumanist approach that
integrates technology and human agency (Wangdi & Shimray, 2025).
In summary, the integration of AI in higher education EFL contexts offers a robust
pathway to enhance linguistic skills and emotional engagement. However, its sustainable
implementation requires a pedagogical shift: moving from viewing AI as a simple shortcut for
production to treating it as a sophisticated partner that requires self-regulation (Xu & Jumaat,
2024), critical oversight, and active learner engagement to be truly effective.
CONCLUSION
The systematic analysis of the selected literature confirms that the integration of Artificial
Intelligence (AI) into higher education EFL contexts represents a fundamental pedagogical shift
rather than a mere technological trend. The evidence suggests that generative AI, conversational
chatbots, and automated writing evaluation (AWE) systems function as effective catalysts for
linguistic development, particularly in enhancing speaking fluency, pronunciation accuracy, and
writing mechanics. Beyond cognitive gains, these tools successfully address the affective
dimensions of learning by lowering anxiety, mitigating boredom through gamification, and
fostering a psychologically safe environment for practice and assessment.
However, this research is not without its limitations. First, the scope of this review was
restricted to 26 articles selected from specific academic databases, which, while rigorous, may
not capture the entirety of the rapidly expanding body of literature on AI in education. Second,
the focus was exclusively on higher education settings; therefore, the positive outcomes reported
here cannot be automatically generalized to K-12 contexts where learner autonomy and digital
literacy levels differ significantly.
Future research must prioritize longitudinal designs that extend beyond a single semester.
It is vital to determine if the linguistic gains and motivation provided by AI sustain themselves
once the novelty diminishes or if they regress, as hinted by some follow-up data.
Vol. 12/ Núm. 4 2025 pág. 3409
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graduate students’ English learning in the AI era. SAGE Open, 15(4), 1–15.
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with chatbots on incidental and collocational vocabulary learning and foreign language
learning boredom. Discover Education, 4, 501. https://doi.org/10.1007/s44217-025-
00977-7
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Abdellatif, M. S., Alshehri, M. A., Alshehri, H. A., Hafez, W. E., Gafar, M. G., & Lamouchi, A.
(2024). I am all ears: Listening exams with AI and its traces on foreign language learners’
mindsets, self-competence, resilience, and listening improvement. Language Testing in
Asia, 14, 54. https://doi.org/10.1186/s40468-024-00329-6
Annamalai, N., Eltahir, M. E., Zyoud, S. H., Soundrarajan, D., Zakarneh, B., & Al Salhi, N. R.
(2023). Exploring English language learning via chatbot: A case study from a self-
determination theory perspective. Computers and Education: Artificial Intelligence, 5,
100148. https://doi.org/10.1016/j.caeai.2023.100148
Asmar, K., El Jai, M., El Jai, Y., & Belfakir, L. (2025). Incorporating AI-generated Duolingo
within collaborative SLL: Spoken English students at FLDM-USMBA as a case study.
LatIA, 3, 317. https://doi.org/10.62486/latia2025317
Delgado, H. O. K., et al. (2020). Artificial intelligence adaptive learning tools: The teaching of
English in focus. Revista Porto Alegre, 11(2), 1–19. Recuperado de
https://repositorio.pucrs.br/dspace/bitstream/10923/27420/2/Artificial_intelligence_ada
ptive_learning_tools_the_teaching_of_English_in_focus.pdf
Dizon, G., & Gold, J. (2023). Exploring the effects of Grammarly on EFL students’ foreign
language anxiety and learner autonomy. The JALT CALL Journal, 19(3), 299–316.
https://doi.org/10.29140/jaltcall.v19n3.1049
Doroudi, S. (2023). The Intertwined Histories of Artificial Intelligence and Education.
International Journal of Artificial Intelligence in Education, 33, 885–928.
https://doi.org/10.1007/s40593-022-00313-2
Duong, T., & Suppasetseree, S. (2024). The effects of an artificial intelligence voice chatbot on
improving Vietnamese undergraduate students’ English speaking skills. International
Journal of Learning, Teaching and Educational Research, 23(3), 293–321.
https://doi.org/10.26803/ijlter.23.3.15
Guillermo Morales, L. E., & Carcausto Calla, W. H. (2025). El uso de chatbots en el aprendizaje
de idiomas: una revisión sistemática. Revista INVECOM, 5(3), 1–15. Recuperado de
https://www.revistainvecom.org/index.php/invecom/article/view/3586/734
Hajihasankhansary, L., & Gilanlioglu, I. (2025). Critical thinking as a key to empowering
graduate students’ English learning in the AI era. SAGE Open, 15(4), 1–15.
https://doi.org/10.1177/21582440251399104
Jalambo, M. O., Çakmak, F., & Akhter, S. (2025). Effects of self-regulated vocabulary learning
with chatbots on incidental and collocational vocabulary learning and foreign language
learning boredom. Discover Education, 4, 501. https://doi.org/10.1007/s44217-025-
00977-7
Vol. 12/ Núm. 4 2025 pág. 3410
Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays?
A review on artificial intelligence in the EFL context. Frontiers in Psychology, 13,
1049401. https://doi.org/10.3389/fpsyg.2022.1049401
Khlaisang, J., & Sukavatee, P. (2023). Mobile-assisted language learning to support English
language communication among higher education learners in Thailand. The Electronic
Journal of e-Learning, 21(3), 234–247. https://www.ejel.org
Kundu, A., & Bej, T. (2025). Transforming EFL teaching with AI: A systematic review of
empirical studies. International Journal of Artificial Intelligence in Education.
https://doi.org/10.1007/s40593-025-00470-0
Liu, J. (2025). Exploring the impact of artificial intelligence-enhanced language learning on
youths’ intercultural communication competence. Humanities and Social Sciences
Communications, 12, 1757. https://doi.org/10.1057/s41599-025-06033-x
Lo, C. K., Yu, P. L. H., Xu, S., Ng, D. T. K., & Jong, M. S.-y. (2024). Exploring the application
of ChatGPT in ESL/EFL education and related research issues: A systematic review of
empirical studies. Smart Learning Environments, 11(50). https://doi.org/10.1186/s40561-
024-00342-5
Lu, C. (2025). AI-generated corpus learning and EFL learners’ learning of grammatical structures,
lexical bundles, and willingness to write. PLOS ONE, 20(7), e0321544.
https://doi.org/10.1371/journal.pone.0321544
Ma, Y., & Chen, M. (2025). The human touch in AI: Optimizing language learning through self-
determination theory and teacher scaffolding. Frontiers in Psychology, 16, Article
1568239. https://doi.org/10.3389/fpsyg.2025.1568239
Moussa, A., & Belhiah, H. (2024). Beyond syntax: Exploring Moroccan undergraduate EFL
learners’ engagement with AI-assisted writing. Arab World English Journal (AWEJ),
Special Issue on ChatGPT (April), 138–155. https://doi.org/10.24093/awej/ChatGPT.9
Murtisari, E. T., Januardi, J. I., Bonar, G., & Kurniawan, D. (2025). Beyond error correction:
Lower-proficiency EFL learners’ engagement with Grammarly. Language Awareness.
Advance online publication. https://doi.org/10.1080/09658416.2025.2585004
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,
Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J.,
Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E.,
McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: an updated guideline
for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Phanwiriyarat, K., Anggoro, K. J., & Chaowanakritsanakul, T. (2025). Exploring AI-powered
gamified flipped classroom in an English-speaking course: A case of Duolingo. Cogent
Education, 12(1), 2488545. https://doi.org/10.1080/2331186X.2025.2488545
Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays?
A review on artificial intelligence in the EFL context. Frontiers in Psychology, 13,
1049401. https://doi.org/10.3389/fpsyg.2022.1049401
Khlaisang, J., & Sukavatee, P. (2023). Mobile-assisted language learning to support English
language communication among higher education learners in Thailand. The Electronic
Journal of e-Learning, 21(3), 234–247. https://www.ejel.org
Kundu, A., & Bej, T. (2025). Transforming EFL teaching with AI: A systematic review of
empirical studies. International Journal of Artificial Intelligence in Education.
https://doi.org/10.1007/s40593-025-00470-0
Liu, J. (2025). Exploring the impact of artificial intelligence-enhanced language learning on
youths’ intercultural communication competence. Humanities and Social Sciences
Communications, 12, 1757. https://doi.org/10.1057/s41599-025-06033-x
Lo, C. K., Yu, P. L. H., Xu, S., Ng, D. T. K., & Jong, M. S.-y. (2024). Exploring the application
of ChatGPT in ESL/EFL education and related research issues: A systematic review of
empirical studies. Smart Learning Environments, 11(50). https://doi.org/10.1186/s40561-
024-00342-5
Lu, C. (2025). AI-generated corpus learning and EFL learners’ learning of grammatical structures,
lexical bundles, and willingness to write. PLOS ONE, 20(7), e0321544.
https://doi.org/10.1371/journal.pone.0321544
Ma, Y., & Chen, M. (2025). The human touch in AI: Optimizing language learning through self-
determination theory and teacher scaffolding. Frontiers in Psychology, 16, Article
1568239. https://doi.org/10.3389/fpsyg.2025.1568239
Moussa, A., & Belhiah, H. (2024). Beyond syntax: Exploring Moroccan undergraduate EFL
learners’ engagement with AI-assisted writing. Arab World English Journal (AWEJ),
Special Issue on ChatGPT (April), 138–155. https://doi.org/10.24093/awej/ChatGPT.9
Murtisari, E. T., Januardi, J. I., Bonar, G., & Kurniawan, D. (2025). Beyond error correction:
Lower-proficiency EFL learners’ engagement with Grammarly. Language Awareness.
Advance online publication. https://doi.org/10.1080/09658416.2025.2585004
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,
Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J.,
Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E.,
McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: an updated guideline
for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Phanwiriyarat, K., Anggoro, K. J., & Chaowanakritsanakul, T. (2025). Exploring AI-powered
gamified flipped classroom in an English-speaking course: A case of Duolingo. Cogent
Education, 12(1), 2488545. https://doi.org/10.1080/2331186X.2025.2488545
Vol. 12/ Núm. 4 2025 pág. 3411
Polakova, P., & Klimova, B. (2024). Implementation of AI-driven technology into education: A
pilot study on the use of chatbots in foreign language learning. Cogent Education, 11(1),
2355385. https://doi.org/10.1080/2331186X.2024.2355385
Qiao, H., & Zhao, A. (2023). Artificial intelligence-based language learning: Illuminating the
impact on speaking skills and self-regulation in Chinese EFL context. Frontiers in
Psychology, 14, Article 1255594. https://doi.org/10.3389/fpsyg.2023.1255594
Robillos, R. (2024). Synergizing generative pre-trained transformer (GPT) chatbots in a process-
based writing paradigm to enhance university students’ writing skill. Journal of
Language and Education, 10(3), 79–94. https://doi.org/10.17323/jle.2024.18708
Sayed, B. T., Bani Younes, Z. B., Alkhayyat, A., Adhamova, I., & Teferi, H. (2024). To be with
artificial intelligence in oral test or not to be: A probe into the traces of success in speaking
skill, psychological well-being, autonomy, and academic buoyancy. Language Testing in
Asia, 14, 49. https://doi.org/10.1186/s40468-024-00321-0
Shen, C., Shi, P., Guo, J., Xu, S., & Tian, J. (2023). From process to product: Writing engagement
and performance of EFL learners under computer-generated feedback instruction.
Frontiers in Psychology, 14, Article 1258286.
https://doi.org/10.3389/fpsyg.2023.1258286
Sumakul, D. T. Y. G., Hamied, F. A., & Sukyadi, D. (2022). Artificial intelligence in EFL
classrooms: Friend or foe? LEARN Journal: Language Education and Acquisition
Research Network, 15(1), 192–207. https://so04.tci-
thaijo.org/index.php/LEARN/article/view/256723/174228
Taeza, J. (2025). The role of AI-powered chatbots in enhancing second language acquisition: An
empirical investigation of conversational AI assistants. Edelweiss Applied Science and
Technology, 9(3), 2616–2629. https://doi.org/10.55214/25768484.v9i3.5853
Üretmen Karoğlu, S., & Doğan, C. (2025). EFL teachers’ insights on incorporating AI in language
education. Journal of Theoretical Educational Sciences, 18(3), 630–657.
https://doi.org/10.30831/akukeg.1644354
Wangdi, T., & Shimray, R. (2025). AI-powered ReadTheory as a self-access learning platform to
enhance EFL learners’ reading enjoyment and comprehension skills: A posthumanist
perspective. Studies in Self-Access Learning Journal, 16(2), 437–460.
https://doi.org/10.37237/160209
Wei, R., Wang, S., & Dong, X. (2023). The impact of automated writing evaluation on second
language writing skills of Chinese EFL learners: A randomized controlled trial. Frontiers
in Psychology, 14, 1249991. https://doi.org/10.3389/fpsyg.2023.1249991
Xodabande, I., Shiri, S., & Zohrabi, M. (2025). Exploring the impacts of an AI-driven
instructional intervention on Iranian EFL learners’ pronunciation skill development.
Discover Education, 4, 307. https://doi.org/10.1007/s44217-025-00782-2
Polakova, P., & Klimova, B. (2024). Implementation of AI-driven technology into education: A
pilot study on the use of chatbots in foreign language learning. Cogent Education, 11(1),
2355385. https://doi.org/10.1080/2331186X.2024.2355385
Qiao, H., & Zhao, A. (2023). Artificial intelligence-based language learning: Illuminating the
impact on speaking skills and self-regulation in Chinese EFL context. Frontiers in
Psychology, 14, Article 1255594. https://doi.org/10.3389/fpsyg.2023.1255594
Robillos, R. (2024). Synergizing generative pre-trained transformer (GPT) chatbots in a process-
based writing paradigm to enhance university students’ writing skill. Journal of
Language and Education, 10(3), 79–94. https://doi.org/10.17323/jle.2024.18708
Sayed, B. T., Bani Younes, Z. B., Alkhayyat, A., Adhamova, I., & Teferi, H. (2024). To be with
artificial intelligence in oral test or not to be: A probe into the traces of success in speaking
skill, psychological well-being, autonomy, and academic buoyancy. Language Testing in
Asia, 14, 49. https://doi.org/10.1186/s40468-024-00321-0
Shen, C., Shi, P., Guo, J., Xu, S., & Tian, J. (2023). From process to product: Writing engagement
and performance of EFL learners under computer-generated feedback instruction.
Frontiers in Psychology, 14, Article 1258286.
https://doi.org/10.3389/fpsyg.2023.1258286
Sumakul, D. T. Y. G., Hamied, F. A., & Sukyadi, D. (2022). Artificial intelligence in EFL
classrooms: Friend or foe? LEARN Journal: Language Education and Acquisition
Research Network, 15(1), 192–207. https://so04.tci-
thaijo.org/index.php/LEARN/article/view/256723/174228
Taeza, J. (2025). The role of AI-powered chatbots in enhancing second language acquisition: An
empirical investigation of conversational AI assistants. Edelweiss Applied Science and
Technology, 9(3), 2616–2629. https://doi.org/10.55214/25768484.v9i3.5853
Üretmen Karoğlu, S., & Doğan, C. (2025). EFL teachers’ insights on incorporating AI in language
education. Journal of Theoretical Educational Sciences, 18(3), 630–657.
https://doi.org/10.30831/akukeg.1644354
Wangdi, T., & Shimray, R. (2025). AI-powered ReadTheory as a self-access learning platform to
enhance EFL learners’ reading enjoyment and comprehension skills: A posthumanist
perspective. Studies in Self-Access Learning Journal, 16(2), 437–460.
https://doi.org/10.37237/160209
Wei, R., Wang, S., & Dong, X. (2023). The impact of automated writing evaluation on second
language writing skills of Chinese EFL learners: A randomized controlled trial. Frontiers
in Psychology, 14, 1249991. https://doi.org/10.3389/fpsyg.2023.1249991
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research on artificial intelligence applications in higher education – Where are the
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https://doi.org/10.22521/edupij.2025.14.51
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English learning: Impacts on listening competencies in Chinese higher education.
Education and Information Technologies. Advance online publication.
https://doi.org/10.1007/s10639-025-13811-2