








































English Language Teaching Educational Journal   ISSN 2621-6485 

Vol. 8, No. 2, August 2025, pp. 118-132  

        https://doi.org/10.12928/eltej.v8i2.14229        http://journal2.uad.ac.id/index.php/eltej/index         eltej@pbi.uad.ac.id  

Designing AI-integrated e-book for English for specific 

purposes (ESP) in health science education 

Surip Haryani a,1,*, Maulida Rahmawati Emha b,2 

a, b Universitas Madani, Jl. Wonosari KM.10 Kr.Gayam Sitimulyo, Piyungan, Bantul, Yogyakarta 55792, Indonesia 
1 yani@umad.ac.id *; 2  maulidaemha@umad.aci.id    

 

* Corresponding author  

A R T I C L E  I N F O 

 
 

ABST RACT   

 

 

Article history 

Received 15 June 2025 

Revised 17 July 2025 

Accepted 4 August 2025 

 This research formulates and tests an AI-integrated e-book to increase 
English for Specific Purposes (ESP) learning by health science 
students in order to enhance speaking performance. Conventional ESP 
materials in health sciences are typically static and non-interactive, 
limiting opportunities for real-time communicative practice. Using a 
Research and Development (R&D) design, the project followed four 
stages: (1) needs analysis involving students, lecturers, and curriculum 
stakeholders; (2) design and development of AI-integrated e-book; (3) 
implementation and testing with 58 students through pre- and post-
tests; and (4) evaluation and refinement after reflection questionnaires. 
Fifty-eight health science students completed pre- and post-tests, 
measured for the fluency, pronunciation, vocabulary, grammar, and 
content/coherence. The findings revealed a strong demand for AI-
integrated ESP materials in need analyses, a notable enhancement in 
speaking ability (pre-test M = 11.71, post-test M = 19.22; p < .001), 
with improvements noted in all performance areas. Feedback from 
reflections showed significant satisfaction, emphasizing advantages 
like accessibility, content relevance, and motivation, while students 
pointed out issues with internet stability and chatbot constraints. These 
results validate the benefits of AI integrated e-book into ESP education 
and provide actionable insights for ESP teachers, curriculum planners, 
and organizations aiming to integrate interactive AI resources in 
healthcare education teaching materials.  

 

© The Authors 2025. Published by Universitas Ahmad Dahlan. 

This is an open access article under the CC–BY-SA license. 

    

 

 
Keywords 

AI-integrated e-book 

English for specific purposes  

Health science education 

peaking performance 

Interactive learning 

 

 

 

 

How to Cite: Haryani, S. & Emha, M. R. (2025). Designing AI-integrated e-book for English for specific 
purposes (ESP) in health science education. English Language Teaching Educational Journal, 8(2), 118-132. 
https://doi.org/10.12928/eltej.v8i2.14229   

1. Introduction  

Artificial Intelligence (AI) and English for Specific Purposes (ESP) education has been more 
imperative than ever in this digital age, spearheading the Sustainable Development Goals (SDG 4: 
Quality Education) by creating inclusive, equitable, and innovative materials for learning. The speed 
and power of innovation in AI technologies like adaptive learning, NLP chatbots, and speech 
recognition bring unmatched promise to improve language learning experiences, especially in some 
fields like health sciences (Haryani, 2025). Against this context, the present study seeks to design and 
validate an AI-powered interactive e-book for health science students to deliver more adaptive, 
interactive, and efficient ESP training. 

ESP, as Hutchinson (2017) suggest, is all about addressing specific learning needs of learners, and 
therefore is a good area to apply AI. Existing literature suggests that ESP learners possess positive 

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attitudes toward AI-based tools, and assign greater motivation for and efficiency in learning 
(Abimanto & Mahendro, 2023; Zawacki-Richter et al., 2019). For example, studies show that AI-
based adaptive learning systems are capable of adapting content presentation as per the level of 
learners' proficiency, thus creating increased interest (Rong et al., 2021). Besides, AI chatbots have 
been shown to enhance oral communication skills via real-time practice conversation (Subiyantoro et 
al., 2023). However, with these encouraging accounts, deployment of AI for ESP material production 
in medicine is still fairly limited, remaining susceptible to shortages in specialized resources and scope 
for maneuver in meeting individual learners' needs (Chang et al., 2023). 

Traditional ESP learning methodologies, especially medicine English, have been considered very 
uninspiring, contextually incomplete, and unable to cater to the different learner profiles (Tapalova & 
Zhiyenbayeva, 2022). The deficits of traditional learning materials necessitate the development of 
new ones in place that can bridge the gap between theory and practice. Interactive e-books 
supplemented with AI are an excellent option for multimedia embedding, real-time feedback, and 
dynamic adjustments based on learner progress (Halkiopoulos & Gkintoni, 2024). Previous studies 
have indicated the possibilities of AI chatbots in language acquisition (Hapsari & Wu, 2022), but few 
have explored their application in medical ESP.  

Drawing on past research studies in 2023 and 2024, where the application of AI by Indonesian 
ESP lecturers was investigated (Du & Gao, 2022; Chuang et al., 2022), the current study suggest a 
new hybrid of adaptive learning, NLP-driven chatbots, and speech recognition in an independent 
interactive e-book format. This mixture would increase students' mastery of medical English 
vocabulary and communication skills at high interest rates. In contrast to past research that mainly 
focused on static e-books or general AI tools, this study suggests a domain-specific AI-fortified 
learning tool measured by both quantitative and qualitative means (Oktafiani et al., 2021). 

The originality of this study is in its focused optimization of AI for use in the generation of 
interactive health sciences e-books with the goal of bridging some content specialization and adaptive 
delivery gaps. The designed model not only promises to enhance language learning outcomes but also 
helps to revolutionize learners' experience with ESP materials, making learning more context-specific, 
relevant, and engaging. 

Recent research has shown that combining interactive e-books with higher-order thinking skills 
(HOTS) tasks can greatly improve learner involvement and mastery of content, especially in science 
disciplines (Sianturi et al., 2021). Intelligent assistants powered by AI for personalized and adaptive 
learning have demonstrated significant potential in customizing instruction to meet the specific needs 
of individual learners (Sianturi et al., 2021; Goel, 2020; Adamu & Awwalu, 2019). In the realm of 
ESP, the integration of bilingual curricula and AI-fueled collaborative learning approaches has been 
noted to enhance both language proficiency and cooperation (Asrifan et al., 2025). Progress in 
generative AI has notably enhanced the creation of customized intelligent tutoring systems (Maity & 
Deroy, 2024), whereas the AI in Education 5.0 framework provides a thorough model for adaptive 
learning settings (Yamamoto et al., 2024). Systematic reviews emphasize the efficacy of AI-driven 
adaptive e-learning in health education (Fontaine et al., 2019; Komolafe et al., 2025) and the promise 
of virtual patient simulation tools in medical (Gellner et al., 2025). Additionally, personalized learning 
methods (Das, 2025), and advanced interactive multimedia systems for e-healthcare (Yamamoto et 
al., 2024) offer chances for further improvement of AI-driven ESP teaching resources.  

Although many studies have investigated the role of artificial intelligence in education, some 
studies have approached it in a fragmented manner. For instance, prior works on AI chatbots 
emphasize their potential to reduce speaking anxiety and promote engagement (Hapsari & Wu, 2022), 
while others have demonstrated the value of adaptive learning systems in personalizing instruction 
(Tapalova & Zhiyenbayeva, 2022). Nevertheless, these studies often focus on a single AI feature in 
isolation, such as chatbots or adaptive platforms, rather than integrating multiple AI capabilities into 
a cohesive learning resource. Likewise, recent studies on ESP e-learning materials highlight the 
effectiveness of interactive e-books (Sianturi et al., 2021), though many do not include AI-based 
feedback or real-time engagement. 

An additional gap exists within the realm of health sciences ESP. Although AI's role in education 
has been extensively examined in general EFL or higher education contexts (Gutiérrez, 2023; Wangdi, 
2024), studies specifically focusing on English for health professionals are still limited. Current ESP 
research often emphasizes needs analysis or traditional materials (Haryani, 2025; Šokčević, 2024), yet 



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there is limited exploration of AI-augmented approaches that integrate domain-specific terminology, 
clinical communication tasks, and tailored practice. This research fills that void by developing and 
experimentally evaluating an AI-driven interactive e-book for ESP in health sciences, combining 
adaptive learning, NLP-driven chatbots, and speech recognition into one platform. This approach not 
only confirms the efficacy of AI for ESP education but also shows how these advancements can meet 
the requirements of stakeholders, such as students, curriculum designers, and ESP instructors 

In this regard, this study responds to three research questions:  

1. How can an interactive e-book using AI be designed and developed for health science ESP 
students?  

2. To what extent is the e-book effective in developing learners' professional English capabilities?  

3. What are the problems and solutions involved in utilizing AI with health science ESP content? 

2. Method 

2.1.  Research Design  

The research utilized a research and development (R&D) instructional technology approach to 
conceptualize and pilot test an AI-based interactive e-book for ESP learning of health science students. 
The design commonly used in instructional technology to produce and validate learning innovations 
mentioned by Gall & Borg in Farhana et al (2021) and  Sugiyono in Buhari (2019). The process 
followed five main stages: (1) needs analysis, (2) design and development, (3) implementation and 
testing, and (4) evaluation and refinement, adapted from established R&D frameworks and recent 
applications in AI-enhanced ESP learning (Sianturi et al., 2021) 

2.2. Participants 

The respondents involved fifty-eight students studying in Faculty of health science in Universitas 
Madani, who were purposively sampled based on prior experience in ESP courses. 

The participants consisted of 58 health science students from Universitas Madani who were 
actively enrolled in English for Specific Purposes (ESP) courses. They represented multiple study 
programs and academic levels: Diploma in Pharmacy (n = 12; semesters 2, 4, and 6), Diploma in 
Midwifery (n = 5; semester 6), Bachelor of Nursing (n = 35; semesters 2, 4, 6, and 8), and Nursing 
Profession Program (n = 5; semester 2). This distribution ensured variation across both disciplinary 
backgrounds and levels of study, ranging from early to advanced semesters. 

Such purposive sampling was considered appropriate because the selected students were those 
most directly exposed to ESP instruction in their curriculum and thus were the most relevant 
population for testing the AI-integrated e-book since they were able to participate in the research after 
hospital practice session finished. Thos who could not participate were mostly at the hospital for 
clinical practice.  

2.3. Sampling Technique   

A purposive sampling method was used, concentrating on students who were currently enrolled in 
ESP classes and had just finished their hospital practicum. This group was chosen due to their 
availability throughout the study period and because they represented the most pertinent population 
to offer insights on the incorporation of AI into healthcare ESP education. Students from the Faculty 
of Health Sciences were not included as they were still completing their practicum placements until 
the end of August, which made their involvement unfeasible. The criteria for including students were: 
(1) being enrolled in one of the three health science programs (Nursing, Midwifery, Pharmacy), (2) 
being available to take both the pre-test and post-test assessments, and (3) agreeing to provide 
informed consent. Curriculum stakeholders and ESP instructors were involved due to their positions 
in curriculum development and the implementation of ESP programs.  

2.4. The Data Collection Instruments 

The data were collected through student needs analysis questionnaire, pre- and post-tests for 
learning outcome, and feedback questionnaire to obtain qualitative feedback on the application of the 
AI-integrated interactive e-book. (scores, descriptive statistics, inferential tests) and qualitative 



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insights (themes from reflections) to comprehensively evaluate the effectiveness and usability of the 
constructed learning object. The data were collected during the research during June – August 2025.  

2.5. Needs Analysis 

The needs analysis stage collected data from curriculum stakeholders, ESP lecturers, and health 
science students using questionnaires to identify learning problems, desired content, and potential AI 
features for ESP (Du & Gao, 2022). The design and development phase adopted iterative prototyping, 
integrating adaptive learning, NLP-based medical chatbots, and speech recognition, which were 
reviewed by ESP experts and instructional designers for content validity and usability (Asrifan et al., 
2025; Maity & Deroy, 2024). 

2.6. Design and Development 

Development was guided by an iterative prototyping methodology from initial content creation 
starting with needs analysis, through AI-function integration, and repeated usability testing iterations. 
Expert evaluators—constituting ESP lecturers, instructional designers, and healthcare professionals—
tested the e-book for accuracy of content, language suitability, and usability. Feedback at every level 
of testing was used to develop more accurate interface design, enhance AI response, and guarantee 
fluid interaction between students and the system 

The AI-enhanced e-book was tailored to address English for Specific Purposes (ESP) health 
sciences students' learning requirements. The material was taken from a health-science-oriented ESP 
course syllabus and supplemented with natural medical dialogues, words, and clinical situations to 
render it functional in real working settings. The end product, accessed via link http://bit.ly/pdf-apple, 
combines three principal AI-facilitated features: 

Adaptive Learning – The system dynamically adjusts the level of difficulty in content as per 
students' current performance so that higher and lower performing students both face challenges and 
support commensurate with their level. 

Natural Language Processing (NLP) Chatbot – Students can practice real-time, AI-supported 
medical communication in English, including simulation of patient encounter, case presentation, and 
healthcare consultation. 

Speech Recognition – This functionality gives real-time pronunciation feedback, allowing students 
to practice their medical English speech skills with clinical vocabulary and role-playing simulations. 

Dual AI Interaction Modes 

Chat Session: Students can type questions or prompts related to the e-book content in a real-time 
chat box. This feature supports open-ended discussions, especially for writing practice, vocabulary 
clarification, and grammar checking. 

Voice Chat Session: Students can converse naturally with AI using either Indonesian or English. 
When used in English, AI responds in a native-speaker accent, serving as a speaking partner for 
pronunciation modeling. AI also provides corrective feedback when students’ pronunciation is 
inaccurate, making it a valuable tool for oral fluency and confidence building. 

The design process took an iterative prototype process, starting with needs analysis, ESP content 
writing, addition of AI capabilities, and cycles of subject matter expert testing. Reviewers from ESP 
lecturers, health science faculty members, and instruction designers examined the e-book in content 
accuracy, language appropriateness, technical ease of use, and AI response. Interface redesigning, AI 
functionalities, and learning support tool enhancements were informed at each iteration, with the 
ultimate output optimized for ESP instruction in health sciences. 

2.7. Implementation and Testing 

The execution stage commenced with giving a pre-test to identify baseline ESP ability of students 
in health science settings. After the test, students received a guided tutorial session during which the 
researchers showed them how to use the AI-enhanced e-book. The briefing consisted of an e-book 
setup introduction, description of each element, and a live simulation of learning using AI through a 
model unit of the e-book. 

  



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Through simulation, the students and researchers replicated using AI functionalities—Chat Session 
for text chat and Voice Chat Session for voice practice—according to the materials presented for 
learning. Students demonstrated how AI is utilized to review material, rehearse pronunciation, 
rehearse medical communication role-play, and get automated feedback. 

Following orientation, students were presented with a couple of weeks of self-study through the e-
book integrated with AI. To assist them round the clock, there was an online forum for discussion 
where students could ask questions, exchange experiences, and pose challenges encountered in 
utilizing the e-book. The researchers assisted in monitoring the group by offering explanations and 
constructing problem-solving forums that are both related to learning content and technical utilization 
of AI features. 

Students took a post-test at the conclusion of the instructional period to assess their learning 
progress and a user satisfaction survey to assess their motivation, perceived value, and overall 
experience with the AI-driven e-book. These results gave both quantitative and qualitative 
assessments of the effectiveness of the implementation. 

Pre-test was given to establish students' baseline ESP competence. Interactive AI-powered e-book 
was implemented on a designated instructional period. Post-test and user satisfaction survey were 
given to ascertain learning gains and motivation. 

2.8. Data Analysis 

Quantitative data were analyzed statistically, and qualitative data were coded and thematic analysis 
adhering to Braun and Clarke’s six-phase method in Taylor (2024). Responses were read multiple 
times for familiarity, then meaningful segments were systematically coded. Codes were subsequently 
categorized into possible themes, which were examined and polished to guarantee consistency. Pre-
test and post-test quantitative data were compared with paired t-tests to check for statistical 
significance using 23 version of SPSS (Statistical Package for the Social Sciences). The open-ended 
reflection responses' qualitative data were examined through thematic analysis. Themes were 
identified and labeled, and corresponding quotes were chosen to exemplify each one. The researcher 
performed the analysis, and a senior lecturer was involved in peer debriefing to improve credibility. 
Emerging themes featured “flexibility in access,” “technical challenges like internet instability,” and 
“suggestions for more diverse multimedia resources.” 

2.9. Evaluation and Refinement 

Students were provided with a guided questionnaire to gather their views, experiences, and 
suggestions on utilizing the interactive e-book powered by AI. The questionnaire had closed-ended 
and open-ended questions to measure usability judgment, entertainment, and learning support 
evaluations and obtain qualitative feedback for improvement. 

The data gathered were analyzed to identify frequent themes, the strengths, and weaknesses. These 
were used as input in fine-tuning the AI-included e-book, e.g., in content presentation modification, 
flexibility of AI functionality, and designing the user interface.  

2.10. Ethical Issues 

The Ethical clearance was obtained from the institutional ethics committee. Participants were 
provided with informed consent and assured regarding confidentiality and voluntary participation. 
The research utilized a research and development (R&D) instructional technology approach to 
conceptualize and pilot test an AI-based interactive e-book for ESP learning of health science students. 
Proof of ethical clearance of KEPK/UMP/170/VII/2025 can be provided upon request. 

3. Finding and Discussion 

3.1. Findings 

1) Needs Analysis 

Needs analysis phase required three respondent groups namely health science students (n=58), 
members of curriculum division (n=10), and ESP lecturers (n=3) to ascertain priorities, challenges, 
and readiness for AI-mediated interactive e-books. Data were analyzed descriptively using 23 version 
of SPSS, and findings are presented in descriptive statistic Table 1.  



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Table 1.  Descriptive analysis of the need for AI integration in English for Specific Purposes (ESP) for 

healthcare  

 N Mean Median Minimum Maximum SD IQR 

Students’ Need 

Analysis Score 
58 20.86 21 6 25 3.21 4 

Stakeholder of 

Curriculum and ESP 

teachers Need Analysis 

Score 

13 21.05 21 16 25 2.16 1.50 

 

The Need Analysis data from students (N = 58) revealed a mean score of 20.86 with a median of 
21, a score range of 6–25, and a standard deviation of 3.21. The distribution was negatively skewed 
(left-skewed) and leptokurtic (high kurtosis), indicating that most respondents obtained relatively high 
scores concentrated around the mean. This suggests that the majority of students perceived a strong 
need for integrating AI into English for Specific Purposes (ESP) learning in healthcare. 

The Need Analysis data from curriculum and ESP lecturers (N = 13) showed a mean score of 21.05 
with a median of 21, a score range of 16–25, and a standard deviation of 2.16. The equality of mean 
and median reinforced that the distribution was fairly balanced, though slightly skewed toward higher 
scores (negative skewness). With a relatively narrow range of values (16–25), most lecturers 
expressed similar assessments, indicating broad agreement that AI integration is necessary in the 
development of ESP instruction for healthcare education. 

This is consistent with Hapsari and Wu (2022) studies that AI chatbots alleviate speaking fear and 
increase interaction. Furthermore, the expressed willingness to embrace AI-based interactive e-books 
affirms Chang et al (2023) findings on educators' adoption of AI tools when exposed to adequate 
training. 

Overall, the needs analysis supports the project's emphasis on creating an AI-compatible 
interactive e-book for ESP in health science, specifically on learning medical terms, adaptive learning, 
and practicing speaking in real time—concerns highlighted as high priority by all of the stakeholder 
groups and backed up in earlier studies (Haryani, 2025; Šokčević, 2024; Gutiérrez, 2023). 

2) Design and Development Output 

The AI-enhanced interactive e-book English for Healthcare Professionals was designed via the 
needs analysis and iteratively prototyped. Figures 1, 2, and 3 depict the most important interface and 
integration features. 

 

Fig. 1.  Home Feature Interactive menu showing, Chat with PDF and Voice with PDF modes, allowing text-

based and voice-based interaction with the book content. 



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Fig. 2.  Chat with PDF Feature of AI Integrated E-book 

 

Fig. 3.  Voice with PDF Feature of AI Integrated E-book 

This integration combines adaptive learning, NLP chatbot for medical conversation imitation, and 
speech recognition for pronunciation suggestions, as recommended by Tapalova and Zhiyenbayeva 
(2022) and Hapsari and Wu (2022) 

According to the needs analysis results, the AI-driven interactive e-book English for Healthcare 
Professionals was created and utilized to highlight required learning needs in medical English 
competency. The material was categorized into thematic units (i.e., Patient Interview, Medical 
Vocabulary, Clinical Case Discussion), and each unit included concise learning objectives, interactive 
exercises, multimedia materials, and AI-driven feedback features. 

There are three major AI-enabled capabilities of the e-book: 

1) Adaptive Learning – Dynamically adjusting task difficulty based on learners' performance 
and progress. 

2) NLP Chatbot – Simulating real-life medical conversation for practice in listening and 
speaking with immediate feedback. 

3) Speech Recognition – Live pronunciation assessment and correction. 

This model aligns with earlier suggestions for technology-assisted and personalized ESP learning 
(Tapalova & Zhiyenbayeva, 2022; Chang et al., 2023) and is in line with the promotion of medical 
communication competence by Hapsari and Wu (2022). Through its inclusion of adaptive learning 
pathways and multimodal interaction, the e-book addresses the dual demands identified in the needs 
analysis of contextual use and engagement of learners. 



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3) Implementation & Testing 

The implementation stage was where interactive AI-powered e-book was incorporated into ESP 
courses of health science students for a specific period of teaching. Pre-test was administered to the 
participants to measure their speaking ability under five categories—fluency, pronunciation, 
vocabulary, grammar, and content & coherence.  

According to the descriptive analysis, the median speaking score for students prior to the 
intervention (pre-test) was 11, with a lowest score of 5 and a highest score of 15. Following the 
intervention, the median score on the post-test rose to 19.5, with a lowest score of 11 and a highest 
score of 23. The interquartile range (IQR) for the pre-test was 2, whereas for the post-test it rose to 3, 
reflecting a difference in the level of progress among students. 

The Wilcoxon Signed-Rank Test produced a significance value (Sig. 2-tailed) of 0.000 (p < 0.05), 
demonstrating a statistically significant difference between the scores of the pre-test and post-test. 
Consequently, the intervention demonstrated efficacy in enhancing the speaking skills of the students. 

Table 2.  Descriptive Analysis of Pre-test and Post-test Speaking Scores  

 N Median Minimum Maksimum IQR 
Sig (2-

tailed) 
Pre-test Score 58 11 5 15 2 

0.000 
Post-test Score 58 19.5 11 23 3 

 

 

Fig. 4.  Comparison Pre-test and Post-test Scores 

This level of improvement is significantly greater relative to earlier studies on these AI-powered 
platforms (Gutiérrez, 2023), and this suggests that adaptive learning, NLP chatbot, and real-time 
speech recognition feedback might have significantly influenced students' speaking skills. These 
results are supplemented by previous assertions that AI can be applied for personalized and interactive 
ESP learning improvement (Tapalova & Zhiyenbayeva, 2022; Hapsari & Wu, 2022), especially if 
developed based on learners' requirements and professional backgrounds. 

4) Evaluation & Refinement 

At the end of the implementation period, the students were required to fill a reflection questionnaire 
in order to assess their learning experience with the AI-boosted interactive e-book. The data of 
reflection were descriptively analyzed in 23 version of SPSS, and the outcomes are shown in Table 3. 

Table 3.  Descriptive Statistics – Student Reflection of AI-integrated e-book  

 N Mean Median Minimum Maksimum SD IQR 
Analysis of Students’s 
reflection on AI 

integrated e-book  

58 76.2 79.5 39 90 13.7 19 

 



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The data indicate that student feedback had a mean score of 76.22 and a median of 79.50, with a 
relatively wide distribution (SD = 13.71; range = 51). Overall, most students provided positive 
feedback on the AI-integrated e-book program for English for Specific Purposes (ESP) in healthcare, 
although there was considerable variation across individual responses. 

Qualitative comments on open-ended questions also provided richness to the assessment: 

1) 1Preferred features: access flexibility, sensitivity of AI chatbot, provision of interactive 
pronunciation and grammar check, and contextualization of medical English words. 

2) Problems mentioned: fluctuating internet connection for impacting audio-based AI 
capabilities, question constraint on chatbot option, and need for more heterogeneous 
multimedia (video/audio). 

3) Suggestions for improvement: adding the voice interaction of the AI to finish Indonesian–
English functionality, adding more comprehensive multimedia assets, and adding more varied 
case scenarios. 

They are in line with previous studies on the motivational and pedagogical advantages of AI-
assisted learning tools (Hapsari & Wu, 2022; Tapalova & Zhiyenbayeva, 2022). The technical 
problems that have been noted will inform development improvement for the second cycle of 
development, such as feature stability improvement, improvement of AI functionality, and diversity 
of interactive content. 

 

Fig. 5.  Average Score of Students’ Reflection 

3.2. Discussion  

The current results align with previous studies on learning innovations involving e-books and AI. 
The incorporation of interactive digital books with tasks that require higher-order thinking  skills,  as  
shown  by  Sianturi et al (2021),  highlights  the  ability  to  enhance  cognitive  involvement  in  ESP  
settings.  Likewise,  AI-powered  customized  and  adaptive  learning  frameworks  noted  by  Sajja et 
al (2023),  and  Goel (2020)  correspond  with  the  adaptive  pathways  utilized  in  the  present  AI-
augmented  e-book.  In  the  design  of  ESP  curricula,  the  bilingual  and  collaborative  learning  
methods  outlined  by  Asrifan  et  al (2025)  provide  additional  backing  for  incorporating  AI  to  



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promote  language  proficiency  and  collaboration.  Furthermore,  the  generative  AI  model  for  
intelligent  tutoring  described  by  Maity and Deroy (2024)  alongside  the  AI  in  Education  5.0  
framework  proposed  by  Ari Irvan  et  al (2024)  offer  conceptual  support  for  enhancing  the  
system's  future  functionalities.  Zawacki-Richter  et al (2019),  Fontaine  et al (2019),  and  Komolafe  
et al (2025)  underline  the  effectiveness  of  adaptive  e-learning  in  training  healthcare  professionals,  
whereas  advances  in  virtual  patient  simulation  (Shaw  et  al.,  2025)  and  (Nofal  et  al.,  2025)  
showcase  wider  application  opportunities.  Ultimately,  the  incorporation  of  smart  interactive  
multimedia  in  e-healthcare  (Tyagi  et  al.,  2021)  strengthens  the  significance  of  multimodal  
resources,  a  concept  already  integrated  into  the  e-book's  existing  design. 

The findings from the needs analysis indicated a robust and uniform interest in AI-assisted 
instructional tools among students, curriculum stakeholders, and ESP instructors. This agreement 
aligns with earlier findings that technology-enhanced learning settings are both sought after and 
progressively anticipated in higher education (Haryani, 2025; Šokčević, 2024). This research builds 
on previous findings by incorporating viewpoints from various stakeholders, thus reinforcing the case 
for adoption at the curriculum level. 

The development and design stage produced an interactive e-book enhanced by AI, featuring 
adaptive learning, a chatbot based on NLP, and capabilities for speech recognition. This integration 
surpasses previous studies that typically focused on an individual AI tool (such as chatbots or adaptive 
platforms alone) by merging various AI capabilities into a single platform (Tapalova & Zhiyenbayeva, 
2022; Chang et al., 2023). This design corresponds with ongoing demands for multimodal and tailored 
learning routes (Hapsari & Wu, 2022) and shows that R&D-driven instructional design can convert 
theoretical models into practical, user-focused solutions for ESP. 

The execution and evaluation phase yielded compelling proof of enhancement: the median 
speaking scores of students rose from 11 in the pre-test to 19.5 in the post-test, with the Wilcoxon 
Signed-Rank Test validating statistical significance (p < 0.05). This increase is significantly greater 
than in certain earlier research on AI-driven platforms (Gutiérrez, 2023). The finding indicates that 
combining adaptive learning, chatbots, and speech recognition could have complementary impacts on 
the speaking advancement of learners. Still, the differences noted in learning gains—where some 
students exhibit minimal or even adverse progress—underscore the necessity for customized 
assistance and deeper exploration of engagement trends among learners. 

The refinement and evaluation process also showed that learners preferred the e-book since it was 
available, pertinent, and autonomous-enabling in nature, as indicated by high general scores for 
reflection (M = 76.22). However, open-ended remarks exposed continued problems in the form of 
unstable internet access, chatbot limitation, and a greater need for multimedia input. These are in 
agreement with earlier work on infrastructural and technical challenges in implementing AI (Du & 
Gao, 2022) and emphasize that the quality of AI-supported learning is contextual factors outside of 
content design that mediate. 

Pedagogically, the implication of the findings has a two-fold impact. First, ESP teachers can deploy 
AI-capable e-books as additional learning materials to respond to the ongoing problem of learning 
medical terminology and real-life speaking practice. Second, curriculum planners can see the 
integration of such AI-capable learning materials as part of organizational efforts to improve ESP 
instruction in health-related areas. Notably, scalability and sustainability require ongoing 
improvement, technical assistance, and teacher capacity development, in harmony with organizational 
preparedness in AI implementation (Amiri et al., 2024). 

Finally, this study is not without limitations. The sample was restricted to one institution and a 
relatively small cohort of health science students, limiting generalizability. The reliance on self-
reported reflections also introduces potential response bias. Future studies could expand to multi-
institutional settings, include longitudinal tracking, and employ mixed measures of engagement and 
performance. Furthermore, ethical considerations regarding data privacy and responsible AI use 
should be more explicitly addressed in subsequent research. 

Overall, this study contributes by demonstrating that an AI-enhanced interactive e-book can serve 
as a feasible and effective medium for ESP learning in healthcare, while also exposing practical and 
pedagogical challenges that need to be addressed to ensure broader adoption. 



ISSN 2621-6485 English Language Teaching Educational Journal 128 

 Vol. 8, No. 2, August 2025, pp. 118-132 

 Haryani, S. & Emha, M. R.. (Designing AI-integrated e-book for English for specific purposes…..) 

4. Conclusion 

This research aimed to create, execute, and assess an AI-powered interactive e-book for English 
for Specific Purposes (ESP) within the health sciences, catering to the requirements highlighted in the 
preliminary analysis and the goals specified in the intro. The findings showed notable enhancements 
in students’ speaking skills, favorable user approval, and a strong alignment between the developed 
resources and learners identified needs, validating the anticipated advantages mentioned initially. At 
the same time, the study revealed variability in individual performance and technical constraints, such 
as internet stability and chatbot limitations, which should be considered in future iterations. Beyond 
confirming the effectiveness of the intervention, the results highlight practical implications for ESP 
instructors and curriculum designers, while also pointing to the potential of scaling this model across 
other ESP domains and health-related programs. Future advancements should concentrate on 
improving feature consistency, broadening multimedia and language capabilities, and implementing 
this model in wider educational settings to boost accessibility and increase learner involvement. 

 

 

Acknowledgment 

The authors gratefully acknowledge the support from the Ministry of Education, Culture, Research, 

and Technology (Kemendiktisaintek) under the PDP research grant scheme. Appreciation is 

extended to the Curriculum Division and stakeholders of Universitas Madani, UNIMUGO and 

STIKES Pemkab Purworejo for granting permission and providing need analysis, as well as to the 

students of the Faculty of Health Sciences Universitas Madani who participated in the study and the 

English lecturers whose contributions and feedback helped ensure the quality and relevance of the 

developed materials.  

 

Declarations 

Author contribution : Surip Haryani was responsible for the entire research project. He also 
led the writing of the manuscript and the collaboration with the 
second author. Maulida Rahmawati Emha participated in the data 
collection, transcription and analysis. She also revised the 
manuscript. Both authors approved the final manuscript.  

Funding statement : This research is funded by the government of Kemenidktisaintek as 
a part of PDP Grand 2025 Program with the contract no 0012/HE.P-
P2M/UM/VI/2025) 

Conflict of interest : Both authors declare that they have no competing interests. 

Ethics declaration 

 

 

 

 

 

: We as authors acknowledge that this work has been written based on 
ethical research that conforms with the regulations of our university 
and that we have obtained the permission from the relevant institute 
when collecting data. The ethical clearance was granted with the 
registered number of KEPK/UMP/170/VII/2025. 

We support ELTEJ in maintaining high standards of personal 
conduct, practicing honesty in all our professional practices and 
endeavors. 

Additional 
information 

: No additional information is available for this paper. 

 

 

 

 



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