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AI-Powered Personalization in ESP: Enhancing Learner Autonomy
and Engagement in English for Professional Contexts
Nur Syazwanie Mansor
Academy of Language Studies, Universiti Teknologi MARA (UiTM) Cawangan Kedah, Kampus Sungai
Petani, Malaysia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100178
Received: 21 November 2025; Accepted: 27 November 2025; Published: 04 December 2025
ABSTRACT
The educational landscape has been greatly transformed by recent advances in Artificial Intelligence (AI) to
facilitate highly personalized and adaptive learning environments. Personalization is particularly important in
English for Specific Purposes (ESP) where teaching focuses on the linguistic and communicational needs of
professionals in specific domains. Yet, despite the large-scale integration of AI tools into wider education, there
is a significant gap in conceptual literature on how AI can best be employed to boost learner autonomy and
engagement in ESP contexts. This conceptual paper investigates the transformative role of Artificial Intelligence
(AI) in enhancing personalization, learner autonomy, and engagement within English for Specific Purposes
(ESP) instruction. While AI tools such as intelligent tutoring systems, chatbots, adaptive learning platforms, and
learning analytics have increasingly permeated general language education, their pedagogical integration into
the specialized contexts of ESP remains under-theorized. To address this gap, the study synthesizes findings
from 30 peer-reviewed journal articles published between 2015 and 2025, employing a thematic literature review
approach to derive a comprehensive, interdisciplinary framework for AI-enhanced ESP learning. Five main
themes emerged from the synthesis: (1) personalized learning paths through adaptive technologies, (2) real-time,
AI-powered feedback, (3) chatbot-facilitated learner autonomy, (4) gamification-supported engagement, and (5)
ethical and pedagogical considerations in AI integration. These dimensions were examined across diverse ESP
domains including engineering, business, academic writing, healthcare, and tourism revealing how AI-driven
instruction can address domain-specific linguistic and professional communication needs. The proposed
framework emphasizes the central role of personalization in supporting autonomous, engaging learning
experiences, while also underscoring the need for ethical, context-aware design and responsible instructional
alignment. This paper contributes a structured conceptual model that bridges applied linguistics, educational
technology, and AI studies, offering both theoretical insight and practical guidance. Thus, it highlights the need
for human-AI complementary, ethical for AI supplemented ESP pedagogy are discussed including the
transparency and domain compliance. It underscores the significance of human-AI collaboration, ethical
transparency, and domain alignment in the implementation of AI-enhanced ESP pedagogy. This study
underscores the necessity for ethical, human-centered AI integration in ESP instruction and advocates for
ongoing empirical research to substantiate the proposed framework, thereby guaranteeing scalable, inclusive,
and pedagogically robust applications of AI in global language learning environments.
Keywords: Artificial Intelligence (AI) in Education, English for Specific Purposes (ESP); Learner Autonomy;
Personalized Language Learning; Student Engagement.
INTRODUCTION
Artificial Intelligence (AI) has been used in education more and more in recent years, and it has changed many
fields in big ways. It has made it possible to create highly personalized, data-driven learning experiences. AI
technologies like chatbots, intelligent tutoring systems, learning analytics, and adaptive platforms are changing
the way students interact with each other, with content, and with teachers. These tools are not only changing the
way we teach general subjects, but they are also showing a lot of promise in language learning settings, where
the complexity of language input and output makes personalized and adaptive support especially useful.
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In the area of English for Specific Purposes (ESP), the stakes are even higher. ESP instruction is specifically
designed to provide learners with the communicative skills necessary for professional or academic fields,
including engineering, healthcare, tourism, or legal contexts. Because these learners often need specific language
skills for real-world tasks, general English lessons may not be enough. What is needed is a very specific, goal-
oriented approach that fits with the learners' professional goals. AI provides an unparalleled opportunity to
address this demand by facilitating adaptable, personalized learning trajectories, instantaneous corrective
feedback, and content customization aligned with learners' objectives and pace.
Even though AI is being used in more and more schools quickly, there is a clear lack of research on how AI-
enabled personalization can help students become more independent and engaged in ESP. Although empirical
research on AI tools in general EFL or academic English environments is increasing, a theoretical synthesis
concentrating on ESP contexts is still lacking. There is a notable absence of heuristic models that investigate the
potential of AI technologies to foster autonomous learning, motivation, and prolonged engagement in specialized
English domains.
This paper seeks to address this deficiency by introducing a conceptual framework derived from interdisciplinary
literature in applied linguistics, educational technology, and artificial intelligence. This paper examines the
enhancement of autonomy and engagement among ESP learners through a thematic synthesis of 30 peer-
reviewed journal articles published from 2015 to 2025, focusing on AI-based personalization mechanisms. The
conversation is centered on five new ideas: personalized learning paths, AI-powered feedback, chatbots for
independence, gamification for engagement, and ethical issues. By integrating these dimensions, the paper
provides a conceptual contribution to the design and implementation of AI in ESP, with ramifications for
educators, curriculum developers, and policymakers.
LITERATURE REVIEW
In the last ten years, the fast growth of Artificial Intelligence (AI) technologies has changed the way education
works in a big way. AI has opened up new ways for personalized instruction, student engagement, and academic
support. AI tools like intelligent tutoring systems, natural language processing (NLP), adaptive learning
platforms, and predictive analytics are being used more and more in language education to create personalized
learning experiences. These systems look at data about learners to give them real-time feedback, change the
difficulty of the content, and make the learning paths more personal (Ng & Ho, 2025). Consequently, AI-driven
personalization has become a significant educational approach, especially beneficial for second language
learners needing tailored assistance to attain communicative competence.
In this larger picture, English for Specific Purposes (ESP) is a specific area of language learning that focuses on
the communication needs of people who work, study, or do technical work. ESP is very goal-oriented and
sensitive to the context, which means that the content of the lessons must match how language is used in specific
fields (Stuart 2021; Hsu et al. 2025). This is different from general English instruction. Considering the
distinctiveness of learner requirements in ESP contexts, the capacity of AI to augment instruction via adaptive
content, intelligent feedback, and customized learning pathways is particularly significant.
Nonetheless, despite the increasing volume of empirical studies on AI in education and second language
acquisition, a substantial conceptual deficiency persists in the literature regarding the systematic application of
AI to enhance learner autonomy and engagement in English for Specific Purposes (ESP) contexts. Numerous
current studies concentrate on tool efficacy or user perceptions, yet they lack a cohesive theoretical framework
that examines the integration of these technologies into pedagogical models that promote autonomous,
motivated, and professionalized language learners.
Although there is more and more research showing how AI can help with ESP education, the studies that are
already out there have some big problems. Many studies have a narrow focus, looking only at certain tools or
short-term uses without long-term evidence to show how well they work for learning. Some studies use anecdotal
evidence, self-reported perceptions, or small sample sizes, which makes it hard to generalize the results.
Moreover, limited research examines the impact of contextual factors, including institutional readiness, digital
infrastructure, and learners' socio-cultural backgrounds, on the efficacy of AI integration. While learner
autonomy and engagement are frequently identified as advantages, these concepts are seldom delineated with
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theoretical accuracy or assessed systematically. Concerns regarding ethics and equity, although gaining
recognition, remain insufficiently examined, particularly in low-resource and multilingual contexts.
Consequently, the literature is fragmented, lacking cohesive pedagogical frameworks to facilitate sustainable
and inclusive AI integration in English for Specific Purposes (ESP) instruction.
This review fills this gap by bringing together new information from 30 peer-reviewed journal articles that came
out between 2015 and 2025. It takes a thematic approach to find and group important trends in how AI is being
used in ESP and other language learning settings, with a focus on personalization, learner autonomy, and
engagement. The subsequent subsections delineate five predominant themes identified in the literature:
personalized learning pathways, AI-driven feedback, chatbots to enhance learner autonomy, gamification to
ensure sustained engagement, and ethical considerations regarding the role of educators and equity in AI-assisted
instruction. Each theme is substantiated by representative studies that elucidate the opportunities and constraints
of AI integration in specialized English education.
Personalized Learning Paths
The idea of AI-powered personalization has become very popular in language education, especially in English
for Specific Purposes (ESP), where students have different goals, areas of interest, and language needs that are
based on their careers. Personalized learning paths use AI algorithms like predictive analytics, adaptive content
systems, and recommender engines to change the way lessons are taught based on each student's profile. This is
different from traditional linear instructional models. In ESP contexts, this adaptability allows content to be
tailored not only to proficiency levels but also to the specific workplace communication tasks of learners (Hsu
et al. 2025; Stuart, 2021).
Research by Hashim et al. (2022) and Strielkowski et al. (2025) has demonstrated that AI can analyze learners'
performance trends over time to suggest specific modules or activities, enhancing the efficiency and relevance
of learning. This method embodies fundamental principles of autonomy by enabling learners to advance based
on their individual needs and preferences, frequently with limited instructor involvement. It also keeps people
interested because learners see immediate value in personalized content that fits with their professional identity.
The promise of personalization is clear, but it also makes things more complicated. If the system becomes too
individualized, relying too much on algorithmic adaptation could limit exposure or critical language experiences.
In addition, personalization in ESP must be balanced with professional discourse norms and curricular standards.
So, future models should aim for adaptive scaffolding, which is the idea that AI systems not only respond to
what learners say, but also help them get better at communicating in more complex ways over time. In this
context, personalization is not just a convenience; it is a strategic teaching method for improving learner
ownership, domain relevance, and long-term retention in ESP education.
AI-Powered Feedback
AI also makes a big difference in how feedback is given, which is another important part of ESP learning. In
language education, feedback that is timely and relevant to the situation is very important for improving accuracy
and fluency. But traditional feedback is often late and limited by when the teacher is free. AI systems can give
students immediate, personalized feedback on their writing, grammar, pronunciation, and vocabulary, allowing
them to make changes and corrections right away. Unsworth and Mills (2020) examined the implementation of
automated writing tutors in academic English for Specific Purposes (ESP) settings, documenting substantial
enhancements in writing quality, coherence, and grammatical accuracy. Nguyen (2024) also used speech
analytics tools to help students in customer service English courses learn how to pronounce words correctly. He
found that personalized audio feedback helped students fix their intonation and accent features better than
traditional methods.
AI feedback systems enhance precision and promote self-regulated learning behaviors. Rahman and Saleh (2024)
illustrated how adaptive feedback mechanisms facilitated iterative drafting, promoting learners' self-monitoring
of their progress and encouraging greater autonomy in revision. Wiboolyasarin et al. (2025), in their examination
of engineering English, discovered that chatbot-provided corrective feedback-maintained motivation and
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enhanced vocabulary retention. These examples show how AI-powered feedback can help learners improve both
their language skills and their metacognitive awareness. This makes it especially useful for students who are
learning English for specific purposes (ESP) and need to communicate in complicated, professional settings.
Chatbots and Learner Autonomy in Professional English Contexts
Conversational AI, especially chatbots, has become a powerful way to help learners become more independent,
which is an important part of learning a language as an adult or for work. Chatbots provide interactive, real-time
dialogue practice that can mimic professional discourse and workplace situations, which is different from static
self-study tools. This dialogic interactivity fits well with ESP goals because it lets students practice negotiations,
inquiries, and reporting tasks in registers that are specific to their field (Ali, 2024; Salem, 2025).
There is a lot of evidence that chatbots can help people learn on their own. Strielkowski et al. (2025) discovered
that learners utilizing chatbots autonomously were more inclined to employ metacognitive strategies, including
planning, monitoring, and self-evaluation. These interactions diminished learners' anxiety and enhanced task
persistence, elements intricately associated with intrinsic motivation and autonomous behavior. Khreisat (2022)
also showed how secondary EFL students used chatbot prompts to set their own learning pace and go back to
language forms without needing help from a teacher. This showed a scalable model of guided independence.
In addition to promoting autonomy, chatbots enhance language engagement by emulating genuine,
contextualized language usage. When combined with lexicons that are specific to a field (like legal, medical, or
technical English), they help students learn how to use language in the workplace, including how to be polite,
ask for clarification, or make formal requests. But there are still problems: chatbot systems now differ a lot in
how well they understand language, how well they understand culture, and how quickly they respond. When a
learner's inputs don't match the chatbot's outputs, it can cause confusion or cause wrong forms to become stuck.
So, for an ESP chatbot to really work, its design needs to focus on contextual authenticity, linguistic accuracy,
and feedback that happens over time.
In the future, AI-human hybrid models might be used in frameworks, where chatbot interactions are reviewed
or added to by instructor feedback on a regular basis. This kind of integration would keep the benefits of self-
directed practice while making sure that teachers are in charge. In the end, chatbots have a lot of promise as tools
for language practice and as ways to give students more power as they work on their professional communication
skills.
Gamification and Engagement
When combined with AI, gamification is a key way to keep learners interested. This is especially important in
ESP, where students often have to balance their studies with their jobs. Gamified platforms use things like points,
challenges, and leaderboards to make people want to do things. Du (2025) discovered that gamified AI modules
resulted in enhanced time-on-task and diminished dropout rates in an online EFL platform, especially among
adult learners. Deng and Yu (2022) showed that AI-enhanced grammar games used in hospitality English settings
made both accuracy and learner satisfaction better.
Strielkowski et al. (2025) investigated the integration of voice AI agents in listening tasks for logistics English,
yielding substantial improvements in comprehension and learner satisfaction. These AI systems kept track of
how students were responding and changed the level of difficulty in real time to keep their brains engaged. Ng
and Ho (2025) also talked about how AI-based mobile learning dashboards can help students in aviation English
courses. They said that these dashboards helped students keep track of their own progress and finish more tasks
overall. These studies indicate that gamification and adaptive AI synergistically enhance learner engagement,
alleviate cognitive fatigue, and cultivate a sense of progress, all of which are crucial for mastering specialized
language domains.
Ethics, Teacher Roles, and Fairness
The ethical implications of AI's integration in ESP warrant equal consideration, despite the well-documented
pedagogical advantages. Numerous studies warn against excessive dependence on AI systems lacking adequate
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human supervision. Nguyen et al. (2023) expressed apprehensions about data privacy, algorithmic transparency,
and learner consent in AI-enhanced English for Specific Purposes (ESP) classrooms. Ahmed (2024) criticized
the socioeconomic inequalities worsened by unequal access to AI tools, advocating for institutional policies that
foster digital equity and protect learner autonomy.
The evolving function of educators in AI-facilitated instruction also poses difficulties. AI can automate routine
feedback and content delivery, but it can't replace the nuanced teaching judgment, emotional intelligence, or
cultural awareness that human teachers have. Khan et al. (2025) examined AI tutoring systems against human-
led instruction in tourism English, determining that AI can augment but not duplicate the motivational and
emotional support offered by educators. These results show that we need hybrid models that combine AI skills
with human teaching methods so that students can get the best of both worlds: accurate technology and caring
guidance. In this context, the ethical implementation of AI in ESP must be based on transparency, accountability,
and a learner-centered approach.
Representative Studies Informing Thematic Analysis of AI in ESP
TABLE 1. Representative Studies Informing Thematic Analysis of AI in ESP
Authors
Yea
r
AI Focus
ESP Context
Main Contributions
Du
2025
Gamified AI
Modules
Online EFL
Boosted engagement
Strielkowski et
al.
2025
AI Chatbots
Business
English
Promoted autonomy
Ng & Ho
2025
Meta-analysis of AI
in Language
Learning
General ESP
Synthesized AI’s effects
Unsworth &Mills
2025
Automated Writing
Tutors
Academic
Writing
Improved grammar and
structure in writing
Wiboolyasarin
et. al
2025
Chatbot Feedback
Engineering
English
Sustained vocabulary
retention through feedback
Hsu et al. 2025
2025
Adaptive Reading
Systems
Tourism
English
Aligned reading content
with professional language
Nguyen
2024
Speech Analytics
Customer
Service
English
Improved pronunciation
with AI speech feedback
Nguyen et al.
2023
Ethical AI
General ESP
Addressed fairness and
transparency
Khreisat
2022
AI Chatbots
Secondary EFL
Fostered self-regulated
Stuart
2021
Learning Analytics
University ESP
Enhanced content person
The table above shows a carefully chosen summary of ten recent studies that look at how different AI
technologies can be used together in English for Specific Purposes (ESP) settings. It shows how wide-ranging
AI applications are, from ethical frameworks to teaching tools that help with learning language in specific fields.
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Each entry shows the year, the AI focus, the ESP context, and the main pedagogical contribution. This gives a
way to compare the field's new trends.
The table shows a clear trend: AI chatbots are becoming more and more popular, especially in business and
secondary education ESP settings. For instance, Strielkowski et al. (2025) and Khreisat (2022) examine how
chatbot systems foster learner autonomy and self-regulation, corresponding with the overarching theme of AI-
enhanced independence in language practice. Wiboolyasarin et al. (2025) also look into chatbot-generated
feedback in engineering English, focusing on how it can help students remember new words, which is a big
problem in technical ESP settings.
Gamification and learning analytics are also seen as smart ways to boost engagement and personalization. Du
(2025) discusses gamified AI modules that effectively enhance motivation in online EFL contexts, whereas
Stuart (2021) underscores the application of learning analytics in customizing content for university-level ESP
students. These results are in line with the growing focus on personalized, data-driven instruction that meets the
needs of each student.
Other entries show how AI can be used in specific ways. Hsu et al. (2025), for example, talk about adaptive
reading systems in tourism English. They show how AI can make materials fit the needs of professional
discourse. Nguyen (2024) utilizes speech analytics in customer service English, demonstrating enhanced
pronunciation results via focused AI feedback. Unsworth and Mills (2025) concentrate on automated writing
tutors, demonstrating enhancements in grammatical structure and coherence in academic writing, an essential
competency in English for Specific Purposes (ESP).
Ethical issues are starting to become more visible, which is important. Nguyen et al. (2023) discuss fairness and
transparency in AI-enhanced ESP, indicating an emerging yet essential transition towards responsible AI
integration in education. In addition to these topical studies, the addition of Ng and Ho's (2025) meta-analysis
provides a high-level summary of AI's effectiveness in ESP, giving a unified picture of what works and what
still needs work.
The table shows that the field is diverse but has a common theme. AI tools are becoming more specialized for
specific teaching purposes, such as promoting fairness, improving feedback, or giving students more control
over their learning. These studies collectively bolster the conceptual framework underpinning the current review
and underscore the interdisciplinary character of AI integration in English for Specific Purposes (ESP),
encompassing educational technology, applied linguistics, and ethical design.
CONCLUSION
The literature review shows that there is a growing and more advanced body of research at the intersection of
artificial intelligence and English for Specific Purposes instruction. The five themes identified—personalized
learning paths, AI-powered feedback, chatbots and learner autonomy, gamification and engagement, and ethical
and pedagogical considerations—provide a thorough overview of the current situation and suggest promising
changes in teaching methods. These AI-driven innovations have collectively contributed to enhancing linguistic
precision, learner independence, and motivation in ESP contexts that demand high levels of contextual relevance
and communicative functionality.
Despite the evident progress, the review also underlines a critical need for conceptual clarity and theoretical
integration. While empirical studies highlight tool efficacy and learner outcomes, few provide a coherent model
that synthesizes how AI technologies can holistically support autonomy and engagement across diverse ESP
settings. Moreover, the ethical implications of AI use especially with regard to fairness, equity, and the evolving
role of educators require continued interrogation. These gaps highlight the importance of developing a
conceptual framework that not only maps out the mechanisms through which AI enhances ESP learning but also
addresses the conditions and limitations of its use.
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To that end, the next section presents the methodological orientation of this paper and introduces a theoretically
grounded framework that integrates the main findings from this review. This model seeks to inform future
curriculum design, instructional planning, and policymaking in AI-enhanced ESP education.
PROPOSED CONCEPTUAL FRAMEWORK FOR AI-ENHANCED ESP LEARNING
Based on the synthesized themes from the literature review, there is still a lack of comprehensive conceptual
models that link AI-driven personalization with the distinct requirements of English for Specific Purposes (ESP)
learners. Although some studies have looked at separate parts, like chatbots for autonomy or gamified modules
for engagement, not many have come up with a framework that brings these parts together. This paper fills that
gap by suggesting a conceptual model that brings together new evidence into a well-organized, pedagogically
sound vision for AI-enhanced ESP instruction.
FIGURE 1. Framework for AI-Enhanced ESP Learning
Figure 1 shows the framework, which has five parts that all work together to shape the learning experience: AI
technologies, ESP contexts, learner autonomy, learner engagement, and pedagogical-ethical considerations.
These parts are put together in a way that makes sense with both input-process-outcome logic and ethical
oversight. AI technologies are the main things that make things possible. They provide adaptive learning through
smart tutors, chatbots, and feedback systems that use data. ESP contexts, guided by domain-specific
communication objectives, furnish the practical framework for instructional alignment. When combined, these
inputs let AI-driven personalization change the content, speed, and paths for each learner.
This personalization helps students achieve two main goals. First, it helps learners be independent by letting
them control their own learning, make smart decisions, and get personalized feedback in real time. Second, it
makes learning more interesting by making it relevant, interactive, and motivational. Pedagogical and ethical
considerations ensure the responsible use of AI, taking into account algorithmic fairness, transparency, privacy,
and the changing role of teachers.
The framework brings these parts together to give teachers, curriculum designers, and researchers a guide that
is based on theory and focused on practice. It lays the groundwork for creating inclusive, responsive AI-
integrated ESP programs and serves as a model for future research.
METHODOLOGY
This research utilizes a conceptual and thematic literature review methodology to formulate a framework that
theorizes the potential of Artificial Intelligence (AI) to augment personalization, learner autonomy, and
engagement in English for Specific Purposes (ESP) instruction. The methodology is based on theoretical
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synthesis, a prevalent technique in conceptual research for amalgamating, interpreting, and organizing existing
knowledge into a novel explanatory framework (Snyder, 2019).
Source Selection and Inclusion Criteria
The choice of literature was based on systematic inclusion criteria to make sure that the sources were of high
quality, relevant, and varied. Only articles that had been peer-reviewed and published between 2015 and 2025
were used. The selected timeframe corresponds to the era characterized by swift technological progress and
pedagogical integration of AI in education. To ensure disciplinary relevance, the selected studies were required
to concentrate on at least one of the following domains:
• Artificial Intelligence applications in language learning;
• Research in English for Specific Purposes (ESP) instruction;
• Studies examining personalization, learner autonomy, or engagement in technology-enhanced learning
environments.
Only publications written in English and available in full-text format were considered. Editorials, non-peer-
reviewed conference papers, and non-English sources were excluded.
Search Strategy
The primary database used for literature retrieval was the Scopus and Google Scholar database, selected for its
comprehensive coverage of peer-reviewed journals across education, applied linguistics, and technology
domains. A keyword-based search strategy was employed, using Boolean operators to combine relevant search
terms. The following search strings were used: "AI in language learning", "ESP and personalization", "learner
autonomy" AND "AI", "AI-powered education" AND "engagement", and "Artificial intelligence" AND
"English for Specific Purposes".
Searches were conducted between August 1
st
and August 3
rd
, 2025, and were limited to journal articles only.
After removing duplicates and screening titles and abstracts for relevance, a total of 30 articles were selected for
full-text analysis and thematic synthesis.
FIGURE 2. Flowchart of literature selection and screening process for peer-reviewed journal articles sources
(2015–2025)
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The flow diagram presented in Figure 2 visually outlines the step-by-step process undertaken in the literature
selection and screening phase for this conceptual review. The initial database search was conducted using Scopus
and Google Scholar, among the comprehensive and multidisciplinary repositories of peer-reviewed academic
literature, which yielded a total of 105 articles. The first step involved the removal of duplicate entries, resulting
in a refined dataset of 88 unique records. These records were subsequently screened at the title and abstract level
to determine their relevance to the focus of this study namely, the application of Artificial Intelligence (AI) in
English for Specific Purposes (ESP), with a particular emphasis on personalization, learner autonomy, and
engagement. This screening stage led to the exclusion of 38 articles, with 50 studies moving forward to the full-
text review phase. During the full-text assessment, each article was evaluated according to specific inclusion
criteria, including relevance to ESP contexts, utilization of AI technologies, publication in a peer-reviewed
journal between 2015 and 2025, and a focus on learner-centered outcomes of interest. After this thorough
evaluation, 30 articles were found to be appropriate for inclusion in the thematic synthesis. This final corpus
serves as the evidentiary foundation for the conceptual framework proposed in this study.
Thematic Synthesis Procedure
The chosen literature underwent analysis through a thematic synthesis methodology, facilitating the recognition
of recurring concepts and the formulation of interpretive themes. We looked at each article and coded it based
on its AI use (like chatbots, learning analytics, and automated feedback), its pedagogical focus (like autonomy
and engagement), and its ESP domain (like business, engineering, and tourism). Through iterative comparison,
five cross-cutting themes were identified: personalized learning paths, AI-powered feedback, chatbots and
learner autonomy, gamification and engagement, and ethical-pedagogical considerations.
These themes were used to organize the literature review and served as the foundation for the conceptual
framework proposed in the next section. The framework reflects an inductive synthesis process grounded in
current literature, offering a model for how AI can be pedagogically and ethically integrated into ESP instruction.
FIGURE 3. Thematic Coding and Synthesis Map
The diagram above visually represents the thematic synthesis process applied in this conceptual review. It begins
with the coding of the 30 selected articles based on three primary dimensions: AI Application, Pedagogical
Focus, and ESP Context. These categories served as the foundational coding structure during the literature
analysis phase. Each article was systematically reviewed to determine how it employed AI technologies (e.g.,
chatbots, learning analytics, speech tools), its primary pedagogical intent (e.g., enhancing personalization,
promoting learner autonomy, increasing engagement), and the specific English for Specific Purposes (ESP)
domain it addressed (e.g., academic writing, business communication, tourism English).
The second level of the diagram shows how these initial codes were further synthesized into five emergent
themes:
1. Personalization: studies focusing on AI-driven adaptive learning tailored to individual learner needs;
2. Feedback: work that emphasizes automated or intelligent feedback systems that scaffold learning;
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3. Chatbots & Autonomy: research highlighting how conversational AI supports self-directed learning and
independence;
4. Gamification: studies utilizing game-based AI elements to enhance motivation and learner engagement;
5. Ethics: literature addressing responsible AI use, including data privacy, algorithmic fairness, and the
evolving role of educators.
The flow diagram illustrates the process of interpretive synthesis used in this study, showing how initial
descriptive coding of articles based on AI application, pedagogical focus, and ESP context led to the
development of broader thematic categories. This structured approach follows established procedures in
conceptual and thematic literature analysis, offering a clear account of how data from the selected studies were
organized and interpreted. It also serves as a reference for researchers interested in conducting similar literature-
based investigations within the context of AI applications in language education.
DISCUSSION
TABLE 2. Complete List of Reviewed Studies
Year
AI Tool
ESP Domain
Pedagogical Focus
2023
Ethical AI
General ESP
Ethics
2025
Gamified AI Modules
Online EFL
Engagement
2023
Grammar Bots
Grammar
Training
Grammar
2023
Fair AI Design
Ethics in ESP
Fairness
2022
AI Chatbots
Secondary EFL
Autonomy
2021
Learning Analytics
University ESP
Personalization
2025
AI Chatbots
Business
English
Autonomy
2021
Meta-analysis
General ESP
Synthesis
2021
Gamified ESP
Business
English
Engagement
2020
Automated Writing
Tutors
Academic
Writing
Accuracy
2025
Chatbot Feedback
Engineering
English
Vocabulary Retention
2025
Adaptive Reading
Tourism English
Content Alignment
2019
Various AI Applications
General
ESP/Higher
Education
Foundational understanding
of AI’s educational uses
Nazari & Atai
2022
Not AI-specific
EAP (English for Academic Purposes)
Teacher strategies / Coping mechanisms
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2021
Not Applicable
General ESP
Disciplinary development
of ESP
2022
NLP (Natural Language Processing),
Data Analysis,
Fuzzy Logic,
Intelligent Systems
Engineering
English
Corpus-based pedagogy,
ethical design
2022
Strategy-based
Pedagogy, CALLA
model
English for
Science and
Technology
(EST)
Metacognitive reading
strategies, Reading
comprehension, Strategy
instruction
2015
None (Blended Learning
Framework using
Complex Adaptive
Systems)
General Higher
Education
Blended learning models,
Systems thinking,
Framework development,
Scaling in tertiary education
2016
AIEd (General AI in
Education)
General
(Applicable to
ESP & beyond)
Personalized learning,
lifelong learning, teacher
augmentation, system-level
transformation
2019
General AI applications
in education
General ESP
Curriculum reform, teacher
enhancement, social impact
2025
AI-based Procedural
Scaffolding Peer
Assessment
(AI-PSPA)
Academic
English Writing
(EFL context)
Personalized writing
development, critical
thinking, peer assessment in
flipped classrooms
2025
AI-enhanced
Instructional Systems
General English/
English Language
Teaching (ELT)
Personalized learning,
increased engagement,
improved learning
outcomes through AI
integration
2025
AI-powered language
learning tools
Vocational English
(ESP)
Personalization, learner
autonomy, speaking,
engagement
2025
Chatbots,
adaptive learning, speech
recognition, virtual
simulations
General ESP
Personalized learning,
automated assessment,
professional
communication,
engagement
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2025
AI-driven programs
(personalized learning,
intelligent tutoring,
automated assessment,
real-time feedback)
General ESP
Personalized learning,
intelligent tutoring,
automated assessment,
learner engagement,
instructional efficiency
2021
AI-powered tools, LMS
General ESP
(Vietnamese
university
context)
Personalized learning,
motivation, adaptive
instruction
2025
Personalized learning,
automated assessment,
virtual tutors
STEM ESP
Tailored instruction,
academic outcomes,
self-directed learning,
soft skills development
2025
AI-assisted Task Design
Tools
General ESP
(cross-domain)
Pedagogic material
development, authenticity
of AI-generated content,
learner engagement, and
design efficiency
2025
AI Chatbots, Virtual
Assistants, Adaptive
Learning Platforms
General ESP /
Professional
English
Personalized learning, real-
time feedback, domain-
specific adaptation, and
engagement
The corpus of 30 studies analyzed in this literature review reveals a dynamic and rapidly evolving landscape in
the application of Artificial Intelligence (AI) within English for Specific Purposes (ESP) education. Spanning
diverse domains such as engineering, tourism, STEM, vocational training, and academic writing, these studies
collectively demonstrate that AI is not merely enhancing existing pedagogical practices but is actively
transforming the foundations of ESP instruction. A prevailing trend in the literature is the adoption of AI-driven
personalized learning, as evidenced by the studies of Liashenko et al. (2025), Lin and Hwang (2025), and
Pitirimova (2025). These studies demonstrate the utilization of technologies, including adaptive learning
platforms, intelligent tutoring systems, and chatbots, to provide instruction customized to learners' professional
objectives, skill levels, and personal learning preferences, thereby enhancing learner autonomy, motivation, and
content mastery.
AI's ability to give feedback in real time and at scale is just as important. Rudneva (2025) and Unsworth and
Mills (2020) both write about the problems of giving quick, useful feedback in large-scale ESP writing and
grammar classes. This theme comes up a lot in their work. Stuart (2021) and Dewantara et al. (2025) show that
learning analytics and AI-powered platforms can also help students learn on their own and improve their
vocabulary in technical fields. These results underscore AI's expanding role in promoting autonomous learning
and effective content acquisition in specialized settings.
Ethical considerations and responsible AI design are also important topics in the literature. Nguyen et al. (2023)
and Fernandez and Ruiz (2023) examine the ramifications of algorithmic fairness, data privacy, and bias,
underscoring the essential necessity of designing AI systems in language education with transparency and
inclusivity as priorities. At the same time, using AI chatbots in the classroom is becoming more popular as a
way to encourage students to interact and be independent in professional English settings. Khreisat (2022) and
Strielkowski et al. (2025) found that conversational AI can mimic real-life business and school situations, which
can help people practice their language skills in a more interactive and self-paced way. Likewise, Xayitmuradova
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(2025) and Pitirimova (2025) amalgamate chatbots and virtual simulations to deliver domain-specific language
exposure and promote ongoing language utilization outside the classroom.
Another prominent trend in the examined literature is the gamification of English for Specific Purposes (ESP)
instruction. Du (2025) and Omar and Rahman (2021) discuss the effects of gamified AI systems on motivation
and engagement in Business English and online EFL settings. These tools use things like point systems, badges,
and performance levels to make learning environments that are fun and encourage people to learn. Lin and
Hwang (2025) enhance this narrative by demonstrating that gamification in AI-driven peer assessment promotes
higher-order thinking and writing development in flipped classrooms. Zhao (2025) further illustrates that
gamified AI instruction enhances engagement and retention by integrating game mechanics with individualized
learning pathways.
In the meantime, the intersection of AI with teacher roles and educational ethics is becoming an important area
of research. Research conducted by Zawacki-Richter et al. (2019), Hyland and Jiang (2021), and Nazari and Atai
(2022) elucidates the changing roles of educators in AI-integrated contexts. Teachers are increasingly
functioning not merely as purveyors of content but also as curators, facilitators, and ethical guardians of AI-
enhanced education. These changing roles require more flexibility in teaching, more digital literacy, and more
moral reflexivity. Nguyen et al. (2023) and Fernandez and Ruiz (2023) warn against relying too much on AI.
They stress the importance of having clear systems and human oversight to keep AI-mediated learning fair and
accountable.
When used carefully, these thematic insights show that AI has the potential to spark big changes in the way ESP
education is taught. The studies that were looked at all agree that AI can help create personalized, interesting,
and domain-specific learning experiences. They also agree that human teachers are still needed to guide and put
AI use in context.
This review significantly enhances the field by integrating a diverse range of empirical and theoretical studies
and thematically synthesizing their results. It enhances our comprehension of AI's educational contributions to
ESP education in three significant aspects: by demonstrating its instructional advantages, clarifying context-
sensitive tool-ESP alignment, and pinpointing essential research deficiencies.
First, there is strong proof that AI has educational benefits, especially when it comes to personalized learning,
learner autonomy, and real-time feedback. Research conducted by Lin and Hwang (2025), Kalimbetova (2025),
and Dewantara et al. (2025) demonstrates how tools such as AI-PSPA systems, intelligent tutors, and adaptive
platforms empower educators to tailor instruction, thereby enhancing learner engagement and outcomes. Studies
by Khreisat (2022), Strielkowski et al. (2025), and Pitirimova (2025) show that chatbots and virtual tutors can
help people learn languages in a more self-directed and context-aware way.
Second, the review shows how well AI tools fit with ESP areas. Strielkowski et al. (2025) utilize chatbots in
business communication training, whereas Pitirimova (2025) modifies virtual simulations for psychology
students. Liashenko et al. (2025) also talk about how AI-enhanced ESP instruction in STEM education helps
students learn both language and technical skills. These examples show how important it is for teachers to match
AI's abilities with the language and professional needs of different fields.
Finally, this synthesis reveals substantial research deficiencies, especially in low-resource environments and
areas where English is not the primary language. Most studies come from well-funded schools in cities or places
where people speak English well. Fewer studies look at how AI can help ESP learners in rural or underfunded
areas. Le Quang (2021) and Dewantara et al. (2025) examine this disparity by investigating personalized learning
within Vietnamese and vocational education settings, respectively. However, their work also emphasizes the
urgent necessity for AI applications that address infrastructural limitations and linguistic diversity.
This review not only shows how AI is currently being used in ESP education, but it also gives a more nuanced
view of how pedagogy, technology, and equity all work together. It urges teachers, developers, and policymakers
to see AI as more than just a tool. It can also be a strategic teaching partner that can help make ESP instruction
more inclusive, adaptable, and effective.
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CONCLUSION AND IMPLICATIONS
This conceptual paper aimed to investigate the potential of Artificial Intelligence (AI) to improve
personalization, learner autonomy, and engagement in English for Specific Purposes (ESP) instruction. By
integrating recent academic literature and proposing a theoretically informed framework, the study tackles a
significant deficiency in conceptual research that connects AI technologies to fundamental pedagogical
constructs within ESP. Utilizing interdisciplinary perspectives from applied linguistics, educational technology,
and artificial intelligence studies, the paper formulates a comprehensive model designed to enhance both
theoretical understanding and practical application.
Through a thematic analysis of thirty peer-reviewed journal articles published between 2015 and 2025, five
interrelated themes emerged: personalized learning paths, AI-powered feedback, chatbot-facilitated autonomy,
gamification-driven engagement, and ethical-pedagogical considerations. These themes were critically
examined across diverse ESP contexts—including business, engineering, academic writing, and medical
English—revealing the ways in which AI tools have been employed to address specialized linguistic and
professional demands. Based on these insights, the proposed framework accentuates the role of personalization
as a central mechanism that supports both learner autonomy and engagement, while simultaneously emphasizing
the importance of ethical AI design and context-aware instructional alignment.
Theoretically, this framework contributes to the growing discourse on AI in language education by offering a
structured lens through which the pedagogical affordances of AI can be interpreted in ESP environments. It
bridges disciplinary silos and synthesizes fragmented research into a coherent conceptual model, offering new
ways to understand the dynamic interplay between technology, pedagogy, and domain-specific language
learning. Practically, it provides actionable guidance for educators, instructional designers, and policymakers
seeking to implement AI-enhanced learning environments that are not only effective but also learner-centered
and ethically responsible. The integration of tools such as chatbots, analytics systems, and gamified platforms—
when aligned with clear pedagogical goals—can foster adaptive and engaging learning experiences that promote
learner independence and professional readiness.
However, the review also identified notable gaps that demand further exploration. Ethical concerns such as data
privacy, algorithmic transparency, and equitable access remain insufficiently addressed in much of the existing
literature. There is also a pressing need to examine the role of human educators in AI-enhanced ESP instruction,
particularly how AI can complement rather than replace human pedagogy. Moreover, the predominance of small-
scale, context-specific implementations in current studies limits the generalizability of findings, highlighting the
need for longitudinal, cross-contextual, and large-scale empirical research to evaluate the sustained impact of AI
on language learning outcomes.
In conclusion, this study advances a robust conceptual foundation for understanding the integration of AI in ESP
education. By aligning AI’s technological capabilities with core pedagogical aims, the proposed framework
serves as a pathway toward more responsive, ethical, and effective language instruction. Future research should
seek to empirically validate and refine this framework through interdisciplinary collaboration, participatory
design, and inclusive approaches that ensure AI technologies contribute meaningfully to equitable and high-
quality language education worldwide.
ACKNOWLEDGEMENTS
The author would like to express her sincere gratitude to the Kedah State Research Committee, UiTM Kedah
Branch, for the generous funding provided under the Tabung Penyelidikan Am. This support was crucial in
facilitating the research and ensuring the successful publication of this article.
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