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GenAI as a Toolkit for English Academic Writing among International Business Students

* * * On the Internet * * *

August 2025 — Volume 29, Number 2

https://doi.org/10.55593/ej.29114int

Benjamin Panmei
Bangkok University International-Language and Culture for Business Department, Thailand
<benjamin.patmarkbu.ac.th>

Yaruingam Phungshok Shimray
Prince of Songkla University, Faculty of Liberal Arts, Thailand
<phungshokshimrayatmarkyahoo.co.in>

Abstract

Generative Artificial Intelligence (GenAI) is increasingly integrated into academic writing globally, but its impact on international students in ASEAN contexts, particularly those facing unique linguistic and cultural challenges, remains understudied. Addressing this gap, this study explores how international business students in Thailand utilize generative AI in English academic writing and examines the challenges they encounter. Using a qualitative narrative inquiry approach, data were collected at the conclusion of a 16-week writing course through narrative frames and follow-up interviews with a subset of students. The study encompassed 43 participants from diverse national backgrounds, including Myanmar, Cambodia, China, Japan, Vietnam, the Philippines, Russia, India, Sweden, and South Korea. Thematic analysis revealed that students used generative AI primarily for data acquisition, idea generation, linguistic refinement, and structural assistance, which supported their writing processes by facilitating data retrieval and offering inspiration. Nonetheless, students also reported significant challenges, including over-reliance on AI that potentially undermines critical thinking, ethical concerns about academic integrity, and limitations of AI in adapting to cultural and linguistic nuances. Findings suggest that although generative AI offers substantial benefits, effective use requires strategies to mitigate dependency and ensure ethical and contextually sensitive application, accentuating the need for AI literacy and institutional guidelines.

Keywords: Generative artificial intelligence, academic writing, international students, business English

The integration of generative artificial intelligence (GenAI) into academic writing has reshaped educational practices (Kim et al., 2024; Teng, 2024a), yet its impact on international students in ASEAN countries remains underexplored despite unique linguistic, cultural, and academic challenges. Studies conducted in Western higher education, such as in Australia, the US, the UK, and Canada, have highlighted both the opportunities and limitations AI brings to academic writing for international students (Farrelly & Baker, 2023; Wang et al., 2023), but the relevance of such findings to those studying at universities in ASEAN is limited due to contextual differences. It has been confirmed that generative AI tools, driven by advanced language models, extend beyond mere grammar correction, providing stylistic, structural, and content-related guidance essential for developing English academic writing skills; for example, recent research by Song and Song (2023)  and Alahdab (2024) has demonstrated AI’s capacity to enhance writing efficacy and adapt to student preferences, facilitating improvements in fluency, coherence, and stylistic nuance. Yet, Wang (2024) notes that the advantages documented in Western settings often lack cultural specificity, thereby accentuating the need for tailored AI applications in diverse educational contexts. Chiu (2024) further recommends AI literacy, ethical frameworks, and curricular redesign to prepare students for AI-driven environments, especially where regional academic expectations, language barriers, and disciplinary demands intersect, as in ASEAN. An urgent need thus emerges for culturally specific research that examines how international students in ASEAN navigate and benefit from generative AI in their academic writing practices.

Thus, the current study fills a notable research gap by investigating the use of GenAI in academic writing among international business students from ASEAN countries enrolled in an international business program at a university in Thailand—a context distinct from the Western-dominated narratives typically found in AI-related research. By collecting qualitative data from students across business, management, and tourism disciplines, the research evaluates both the benefits—such as enhanced productivity, targeted language assistance, and specialized terminology support—and the limitations, notably cultural misalignment and the undermining of critical thinking skills, associated with generative AI. Importantly, the Thai educational framework offers a valuable non-Western ASEAN perspective by revealing often-overlooked socio-academic and disciplinary challenges, as it is shaped by the Thai Qualifications Framework for Higher Education (TQF:HE), which seeks to align graduate competencies with labor market expectations across five domains—ethical reasoning, disciplinary knowledge, cognitive and analytical abilities, interpersonal responsibility, and technological communication—while simultaneously reflecting Thailand’s ongoing negotiation between global modernization and the preservation of cultural identity (Rhein, 2016; Thankdenchai & Heesawat, 2016).

Furthermore, prior studies such as those by Chan and Hu (2023), Kanont et al. (2024), Waluyo and Kusumastuti (2024) and Zaim et al. (2024) emphasize the positive perceptions of generative AI within Asian higher education in Hong Kong and Thailand, showing its potential for personalized learning and research writing support. Nevertheless, issues related to academic integrity and the potential for reduced critical thinking, as noted by Farrelly and Baker (2023), remain a concern across both Western and Asian educational contexts. Despite ongoing research, the specific impacts of generative AI on international students within Asian academic settings, particularly in discipline-specific contexts, are less understood, indicating a significant gap in the literature. Hence, the following research questions guide the study:

  1. How do international business students from ASEAN countries in Thailand utilize generative AI as a toolkit for their English academic writing?
  2. What challenges do international business students from ASEAN countries in Thailand encounter when using generative AI as a toolkit in their English academic writing?

Literature Review

Generative AI and Academic Writing

Generative AI technologies, particularly large language models (LLMs), have become indispensable in enhancing the academic writing capabilities of students encountering language barriers, aiding in structuring, refining, and elevating second-language writing. As corroborated by comprehensive review studies on the application of AI in academic writing, as well as research conducted by Khalifa and Albadawy (2024) and Teng (2024a) on ChatGPT’s role in English as a Foreign Language (EFL) writing, the integration of such tools has demonstrated notable efficacy. A mixed-method study by Rad et al. (2023) involving Persian students from two distinct English Composition courses at an Iranian English language institute revealed substantial improvements in writing outcomes, student engagement, and feedback literacy among those utilizing AI applications, with participant feedback further attesting to the AI’s positive impact on the learning experience. In parallel, Barrett and Pack (2023) examined the perceptions of both educators and students at a public research university in the United States regarding the integration of generative AI across various stages of the writing process, such as brainstorming and revising, revealing minor discrepancies in acceptance levels and highlighting a pervasive institutional unpreparedness to effectively incorporate these technologies. Expanding this inquiry, Malik et al. (2023) conducted research across 25 tertiary institutions in Eastern and Central Indonesia, where students praised the role of AI tools in enhancing grammar, detecting plagiarism, and facilitating language translation, attributing these technologies with bolstering their self-efficacy and deepening their understanding of academic integrity. Despite these promising findings, concerns persist regarding the potential of AI to undermine creativity and critical thinking, thereby emphasizing the necessity for a balanced and thoughtful integration of AI technologies within educational contexts. In response to such concerns, Sasaki (2023) advocates for the strategic application of AI tools in conjunction with students’ native linguistic skills, proposing that, when used effectively, AI can advance second-language writing proficiency while simultaneously preserving the development of critical academic competencies, thereby fostering a pedagogical environment that upholds the complexity and integrity of the educational process.

Generative AI tools have become integral to English language learning for non-native speakers, who encounter specific linguistic and cognitive challenges in academic settings, as evidenced by a quasi-experimental study involving EFL students enrolled in a Bachelor’s degree program at a national university in China (Song & Song, 2023). Serving dual purposes, these tools not only enhance productivity but also substantially improve language proficiency. Further substantiating the utility of GenAI, Kohnke’s (2024) qualitative study in Hong Kong reports that EFL students perceive these tools as both comprehensive and authoritative, providing detailed explanations and contextual insights that are crucial for honing writing skills. On the other hand, Wang (2024) explores the nuanced challenges faced by both native and non-native English speakers in the United States, identifying dilemmas such as balancing the enhancement of writing capabilities with maintaining an authentic voice and weighing the traditional learning experiences against the innovative opportunities facilitated by AI integration. Through the strategic application of AI, international students, particularly in Thailand, where English proficiency greatly varies, can significantly improve their linguistic abilities, thus aligning with global academic standards and cultivating a professional voice suitable for the international business landscape (Ou et al., 2024). By bridging the gap between linguistic barriers and academic success, generative AI not only empowers students in English-based business programs but also sets a foundation for more equitable educational opportunities and enhanced global communication skills.

Generative AI in Business English Education: A Tool for Domain-Specific Writing

The integration of generative AI into business English education is particularly pertinent, considering the complex expectations for business students to demonstrate both language proficiency and domain-specific expertise. Generative AI applications effectively address these dual requirements by generating vocabulary and contextually accurate references aligned with business discourse, a crucial function given the field’s stringent demands for precise terminology, persuasive language, and structured logic, as confirmed by Kumar et al. (2024), who explored the perspectives of academicians and researchers regarding AI usage in higher education in India. A global survey conducted by Cardon et al. (2024) identified essential competencies for business professionals in the AI-driven era, highlighting that generative AI is widely applied in research, ideation, drafting business communications, and summarizing or revising texts. In line with these findings, Khalil and Pipa (2022) examined the practical use of GPT models in business writing, demonstrating that such models generate grammatically accurate, topic-relevant text, thus enhancing the writing process by leveraging extensive data. For international students in Thailand, the challenge of expressing complex business concepts in English is intensified by cultural and linguistic differences; however, AI tools help scaffold this process by enabling students to craft coherent arguments that adhere to professional standards of business English (Kanont et al., 2024). Despite these promising applications, the pedagogical potential of AI in supporting international business students remains underexplored, suggesting a significant research gap that requires further investigation into its efficacy and implications across diverse educational contexts.

A synthesis of current research on generative AI in academic writing highlights its significant role in enhancing productivity, reducing writing time, and improving output quality, as demonstrated in a study by Noy and Zhang (2023) involving 444 experienced, college-educated professionals across multiple countries. Beyond its contributions to academic writing efficiency, generative AI supports critical research functions such as data analysis and literature review, embedding itself as an indispensable resource throughout the research process (Marchandot et al., 2023). Nevertheless, despite its benefits, ethical concerns surrounding academic integrity persist, necessitating cautious and well-regulated integration (Chan & Hu, 2023). Studies by Yusuf et al. (2024), which surveyed participants from 76 countries, reveal widespread familiarity with generative AI for tasks such as information retrieval and paraphrasing and divergent perceptions of its ethical implications, often shaped by cultural contexts. Such findings emphasize the importance of developing context-sensitive ethical guidelines to address these concerns effectively. While students generally embrace generative AI for its capacity to enhance productivity and offer personalized support (Malik et al., 2023), instructors often remain skeptical, reflecting a generational divide in acceptance and adoption (Chan & Lee, 2023). Further research by Nguyen et al. (2024) involving doctoral students from Finland and New Zealand illustrates that interactive engagement with generative AI leads to superior writing outcomes compared to a linear, passive approach. The existing research confirms generative AI’s transformative potential in education while emphasizing the need for culturally informed policies and ethical frameworks to ensure responsible use.

Challenges of Generative AI in Academic Writing

Generative AI offers well-documented productivity benefits in academia, but it also raises significant concerns about its impact on critical thinking and academic integrity. Essien et al. (2024) conducted a mixed-method study within UK business schools, using Bloom’s taxonomy to assess critical thinking skills, and revealed that although AI facilitates lower cognitive tasks such as memorization of facts, explanation of ideas, and application of concepts to new situations, its efficacy diminishes at the taxonomy’s higher levels—analyzing and evaluating—where deeper cognitive processes are required. Participants in the study acknowledged the positive effects of AI on their critical thinking abilities, yet the technology’s limitations in fostering advanced analytical skills were evident. Moreover, in the field of business management education, there are persistent concerns about AI’s reliability, accuracy, and ethical implications in higher education, particularly how it may reduce students’ engagement with complex cognitive processes crucial for developing deep analytical skills (Valcea et al., 2024). In academic writing, Wang (2024) identified dual dilemmas faced by students using generative AI: balancing the enhancement of writing quality with the maintenance of their authentic voice and reconciling the potential loss of traditional learning experiences with the emergence of novel learning opportunities presented by AI. Compounding these issues, Song (2024) notes that the widespread use of AI in higher education raises challenges for maintaining academic integrity, as instances of plagiarism and cheating have increased, despite students’ awareness of these negative outcomes.

Furthermore, although GenAI is lauded for enhancing writing skills and language refinement, it introduces complex challenges related to cultural sensitivity, rhetorical expectations, and inherent biases. Wee and Reimer (2023) compared human-written essays and AI-generated essays across four languages—English, Malay, Mandarin, and Japanese—and revealed limitations in AI detection tools, particularly their inability to accurately distinguish between human-written and AI-assisted essays. Their study found that translated or paraphrased non-English human texts were often mistakenly flagged as AI-generated, accentuating the need for academic policies that clearly differentiate between AI-generated and AI-assisted work to protect non-English scholars. Besides, Liang et al. (2023) emphasize fairness issues, noting that GPT detectors frequently mislabel non-native English writing as AI-generated, raising concerns about equitable treatment in evaluative settings. The lack of agency, metacognition, and nuanced language understanding in generative AI restricts its rhetorical flexibility, often promoting language uniformity and reinforcing biases and structural inequalities (Kuteeva & Andersson, 2024). Nevertheless, when strategically applied, AI can support L2 writers by leveraging native language literacy to bridge proficiency gaps (Sasaki, 2023), and advancements in multimodal AI hold promise for developing multimodal and prompt literacy skills (Kang & Yi, 2023). Despite these advances, AI writing assistants often struggle with content density and nuanced communication (Rahmi et al., 2024), suggesting a need for strategies that both optimize AI’s educational benefits and mitigate its limitations.

Methods

Research Design

The study utilized narrative inquiry as its primary research design, chosen for its capacity to capture the complex, subjective experiences of international students integrating generative AI within academic writing courses. As a qualitative methodology focused on lived experiences, narrative inquiry allows for a nuanced examination of how students interpret and incorporate AI tools into their learning, highlighting the interplay between personal identity, technological adaptation, and academic development (Connelly & Clandinin, 1990). By analyzing the temporal, relational, and situational aspects of student interactions with AI, narrative inquiry reveals how these tools shape individual academic trajectories and influence evolving identities. Attuned to personal, cultural, and institutional contexts, narrative inquiry extends beyond surface observations to uncover the deeper meanings students ascribe to AI, making it an ideal framework for exploring the subjective dimensions of learning with technology (Courduff & Moktari, 2022. Widely applied across fields such as education, healthcare, and technology integration, narrative inquiry has elucidated themes ranging from vocational learning experiences (Daniels, 2008) to the impact of GenAI on creativity and critical thinking (Kartal, 2024). Through this approach, the study offers a sophisticated understanding of generative AI’s role in fostering personal and academic growth in international education settings.

Research Context and Participants

The study was conducted at a Thai university offering international business English programs and received ethical approval from the relevant institution (Reference no: 416710035). The participants were international students from ASEAN countries enrolled in an international business program. A convenience sampling method was employed, utilizing an accessible English writing course. The sample consisted of 43 students, predominantly female (65.12%, n = 28), with male students comprising 27.91% (n = 12), and a small number (6.98%, n = 3) preferring not to disclose their gender. The mean age was 20.26 years (SD = 2.26), suggesting a young demographic primarily in early adulthood. Participants represented a range of nationalities, with a notable proportion from Myanmar (23.26%, n = 10), followed by an additional 20.93% (n = 9) also listing Myanmar as their country, possibly indicating a subset or data duplication. Cambodia accounted for 13.95% (n = 6) of the sample, while China, Japan, and other countries contributed smaller proportions, including participants from China (9.30%, n = 4), Japan (6.98%, n = 3), and various other nations such as Vietnam, the Philippines, Russia, India, Sweden, and South Korea, each representing 2.33% (n = 1).

Regarding English proficiency, 44.19% (n = 19) rated their proficiency as “Good,” while 32.56% (n = 14) assessed themselves as “Average.” Smaller percentages reported higher and lower levels of proficiency, with 13.95% (n = 6) rating themselves as “Very Good,” 6.98% (n = 3) as “Poor,” and 2.33% (n = 1) as “Very Poor.” In terms of generative AI tools utilized, “ChatGPT” was most frequently cited, with 12 mentions, followed by “Grammarly” (8 mentions), and varying forms of “Chat GPT” or “Chatgpt” appearing a total of 9 times, highlighting common usage of generative AI for writing assistance. Other tools, such as “Quillbot” (3 mentions), “Bard,” “Notion,” “Google Translate,” and various other chatbots or grammar checkers, were noted in lesser frequencies, reflecting diverse AI tool usage among the participants.

Instructional Design

The international students in this study were enrolled in an English academic writing course within the International Business English program at a Thai university. The course provided a structured foundation in academic writing, focusing specifically on narrative, descriptive, and expository styles, thus equipping students with versatile skills necessary for meeting varied communicative demands in both academic and professional environments. Spanning 16 weeks, the course emphasized a progressive approach, beginning with foundational paragraph structures and advancing toward a nuanced mastery of each writing style. Course materials were tailored to reflect business-related themes, aligning with students’ academic and career backgrounds. By systematically integrating narrative, descriptive, and expository writing, the course enabled students to build upon their skills incrementally, fostering not only technical accuracy but also strategic organization and effective argumentation. Peer-assisted learning reinforced this skill-building process, as regular peer review sessions encouraged students to engage critically with each other’s work, honing their analytical and evaluative abilities by identifying strengths and areas for improvement in one another’s writing.

Complementing its focus on skill development, the course utilized a blend of instructional methods including lectures, interactive discussions, and peer-editing exercises to bridge theory with practical application. AI tools such as Grammarly, Quillbot, and Wordtune were introduced as supplementary aids to enhance students’ drafts, promoting self-sufficiency and familiarity with technological tools prevalent in professional environments. To ensure academic integrity and originality, the use of ChatGPT was restricted during peer-review sessions to encourage personal engagement and critical thinking. Regular peer reviews and feedback sessions were integral, providing students with opportunities to engage critically with each other’s work, thereby honing their analytical and evaluative skills. Each week was meticulously structured to prepare students for cumulative assessments, including quizzes and final evaluations, that tested their ability to coherently apply the writing strategies learned. Through this structured approach, the course not only enhanced the students’ writing capabilities but also instilled a disciplined approach to learning, fostering a deep, practice-oriented understanding that students could apply in their subsequent academic and career pursuits.

Instrument and Data Collection

This study employed a qualitative research design grounded in narrative inquiry to explore how international business students in Thailand use generative AI applications as a toolkit in their English academic writing course and to identify the challenges they encounter. Data were collected at the end of the semester following the completion of a comprehensive writing course, allowing students to reflect on their entire experience. The primary data collection instruments included narrative frames and semi-structured interviews, designed to capture both the breadth and depth of students’ learning experiences and interactions with generative AI tools.

The narrative frames asked students to elaborate on two focal points: their use of generative AI in their academic writing and the challenges they faced in incorporating AI into their writing practices. Students narrated their experiences online, a method that afforded them the flexibility to reflect on their learning without time constraints and facilitated authentic, detailed responses. To gain further insights, researchers invited students to participate in follow-up interviews, and five students volunteered. Conducted via online video conferencing to streamline transcription and ensure the accuracy of participants’ responses, these interviews aimed to probe deeper into each student’s narrative, uncovering the nuances of their experiences with generative AI. The interviews were designed to validate and expand upon themes emerging from the narrative frames, providing richer data through in-depth discussions.

To ensure the validity and reliability of the data, multiple steps were taken. The narrative frames and interview questions were developed based on the research questions and reviewed by two experts in qualitative methodology and TESOL to establish content validity, ensuring that the instruments comprehensively addressed students’ experiences and challenges with generative AI. To enhance reliability, the researchers maintained a detailed audit trail throughout the data collection and analysis process, documenting each stage to ensure consistency. Furthermore, member checking was implemented during the interviews, allowing participants to clarify or expand on their responses, thereby reinforcing the credibility of the findings. This multi-instrument approach, supported by rigorous validity and reliability measures, provided a robust framework for examining the role of generative AI in English academic writing among international business students in Thailand.

Data Analysis

The research questions guiding this analysis focused on how international business students in Thailand utilize generative AI as a toolkit in their English academic writing course and what challenges they encounter in this process. To address these questions, data from students’ narrative frames and interviews were examined using thematic analysis, following the procedures recommended by Clarke and Braun (2017). The analysis began with in-depth familiarization with the data through repeated readings, enabling a thorough understanding of students’ experiences with AI tools in their academic writing. Initial coding was conducted inductively, capturing patterns related to both the functional use of AI and the difficulties students faced. Codes were then grouped into emergent themes, with each theme reviewed for coherence and alignment with the research questions. The following section presents the detailed results.

Results

How do International Business Students from ASEAN Countries in Thailand Utilize Generative AI as a Toolkit for Their English Academic Writing?

The emergent themes of students’ use of GenAI in academic writing
Figure 1. The emergent themes of students’ use of GenAI in academic writing

As shown in Figure 1, four themes emerged from the thematic analysis of international students’ use of GenAI in academic writing, detailed below:

Theme 1: Data and Research Support

Generative AI was widely regarded by students as a valuable tool for streamlining access to credible information, thereby facilitating more efficient engagement with academic writing tasks in fields such as business, management, and tourism. Rather than perceiving AI as a mere search engine, participants described its capacity to serve as an adaptive assistant capable of delivering context-relevant data, statistics, and conceptual explanations, ultimately reducing the time and cognitive effort required for preliminary research. For example, a student from Japan (Japan 1) noted, “I usually use [AI] to find the data and statistics, especially when working on complex topics that require evidence. It’s like having a research assistant that accelerates my process.” This interpretation aligns with perspectives from other ASEAN students who emphasized the utility of AI in efficiently generating foundational content that supports more analytical and argument-driven writing. A Cambodian student (Cambodia 1) stated, “The AI helps me quickly look up necessary data for my papers; it’s like a shortcut to the information I need for solid, well-supported arguments,” revealing an instrumental orientation toward data quality and relevance. Similarly, a student from Myanmar (Myanmar 1) shared, “It’s a supportive tool that I use to help with data-driven parts of my assignments, where I need accurate facts or references without spending hours searching,” which reinforces the theme of time-saving efficiency.

Beyond general data retrieval, students articulated a more sophisticated use of generative AI for domain-specific knowledge acquisition. One student (Interviewee 1) reflected on the utility of AI in demystifying complex concepts in investment and financial planning, stating, “For me, maybe like information on how to invest properly, how to start investment, that kind of thing. ChatGPT explains it in a way I can understand well, so I use it for that.” Another student (Interviewee 2) reported relying on AI to decode discipline-specific terminology: “Sometimes business terms are a little bit difficult for me, so I use AI to make sure I’m on the same page as my teachers.” Such comments suggest not only a functional use of AI for information access but also a targeted application that reflects deeper engagement with disciplinary discourse.

Importantly, responses from students beyond the three national groups further substantiate this theme. For instance, a student from China (China 1) described using AI to “compare several theories before deciding which one fits best for my assignment,” illustrating an evaluative use of the tool. A Vietnamese student (Vietnam 2) mentioned, “It saves me when I don’t understand the topic in the lecture. I ask ChatGPT and it gives me a summary I can build on,” highlighting how AI supports knowledge scaffolding. These varied perspectives affirm that students across different cultural and educational backgrounds leverage generative AI not only to locate information but also to enhance their understanding and application of complex academic content. The qualitative analysis results therefore point to a pattern of use, where generative AI is integrated into both the mechanical and intellectual dimensions of research writing, enabling students to meet the epistemic demands of their disciplines with greater confidence and efficiency.

Theme 2: Idea Generation and Inspiration

The second emergent theme from the qualitative data indicates students’ strategic use of generative AI as a catalyst for idea development, particularly in assignments demanding creativity, organization, and conceptual flexibility, such as those in business and tourism studies. Across narratives and interviews, students described AI not merely as a tool for idea retrieval but as a cognitive scaffold that alleviated the pressures of writer’s block by offering conceptual entry points, organizing fragmented thoughts, and suggesting alternative directions. Rather than substituting their original thinking, students positioned AI as a provisional support system that stimulated their ability to formulate and expand ideas. A student from Cambodia (Cambodia 2) reflected, “Sometimes I use it to help me summarize and gather ideas for my writing, especially when I don’t know where to start or if I’m unsure how to proceed with a topic,” indicating an intentional use of AI during the initial stages of writing. Similarly, a student from Myanmar (Myanmar 2) explained, “AI gives me new ideas I hadn’t thought of—it’s like a brainstorming partner that helps organize my thoughts in a way that feels manageable,” conveying a perception of AI as a collaborative force rather than an intellectual replacement. A comparable view was expressed by a Japanese student (Japan 2): “It really helps when I’m out of ideas; AI can provide some starting points that I then develop further, making the whole writing process smoother and less stressful.”

Importantly, students from additional national contexts echoed these sentiments, further validating the pattern. A Chinese participant (China 2) noted, “Sometimes I use it to help me summarize and gather ideas,” revealing a cross-cultural pattern of AI-mediated ideation. In a related comment, a Vietnamese student (Vietnam 2) highlighted how AI facilitated group discussions and topic generation, particularly when students were unsure how to begin. However, despite general approval, the interview data introduced a more critical dimension. Several students articulated concerns about overdependence, recognizing that frequent reliance on AI could undermine cognitive autonomy. One participant remarked, “If I’m stuck with a 500-word essay and only have 300 words, I might ask AI for more ideas, but then I make sure to change it to my own words” (Interviewee 5), demonstrating a conscious effort to retain ownership over the writing process. Another observed, “AI can make me lazy if I use it too much; it could stop me from thinking critically about my assignments” (Interviewee 5), pointing to an emerging awareness of the risks of intellectual complacency. Collectively, these findings reveal a nuanced engagement with generative AI—one that reflects both appreciation for its ability to spark initial thinking and caution toward its potential to inhibit deeper cognitive effort.

Theme 3: Grammar and Writing Structure

The third theme centers on students’ deliberate use of generative AI to improve the grammatical accuracy and structural clarity of their academic writing. For many international students, particularly non-native English speakers, producing syntactically sound and professionally structured texts remains a persistent challenge. Generative AI, especially grammar-checking tools and sentence-restructuring features, was frequently employed as a form of linguistic scaffolding—allowing students to identify and correct grammatical errors, rephrase awkward sentences, and ensure overall textual coherence. One student from Japan (Japan 3) remarked, “Personally, I only use AI when I most need it, especially with grammar checks to make sure everything sounds right and is easy to read,” signaling a purposeful approach to refining written output. Similarly, a Cambodian participant (Cambodia 3) shared, “AI helps me structure my sentences better, especially when I’m not confident with how it sounds or if I worry about making grammatical errors,” reflecting a reliance on AI to compensate for uncertainty in language performance. A student from Myanmar (Myanmar 3) echoed these sentiments, stating, “It’s useful for improving grammar and sentence flow, so my papers look polished and are more readable for professors,” emphasizing AI’s perceived impact on the professionalism of their academic writing.

Broader analysis of student responses from other countries reinforced this pattern of functional use. For instance, one respondent (Myanmar 7) indicated, “I checked my grammar using AI. Second way is to ask AI to correct my writing,” which demonstrates both proactive and reactive uses of AI for language refinement. In a related narrative, a student (Myanmar 5) explained, “I use a lot of Grammarly. I put that in my Google Docs,” suggesting routine integration of AI into their writing workflow. These examples illustrate how AI has become an embedded support mechanism for students striving to meet academic language standards, particularly in institutions where English-medium instruction is required.

The interviews offered deeper insights into students’ evolving relationships with AI-supported grammar tools. Several interviewees reflected on their shifting dependence, noting that such tools were more heavily utilized during secondary education, when foundational language skills were still being developed. For example, Interviewee 1 stated, “I used Grammarly a lot in high school to make sure my essays were correct. Now I use it mostly to polish my writing, but I rely less on it than I did before,” suggesting a progression toward greater linguistic autonomy. Another student (Interviewee 3) remarked, “Sometimes, if I’m not confident in how my sentences sound, I’ll use AI to help, but I’m more selective with it now than in the past,” indicating a more critical and measured use of AI as their proficiency matured. These findings indicate a complex developmental trajectory in students’ engagement with AI—beginning with dependence for error correction and evolving toward strategic use for final refinement—demonstrating both linguistic self-awareness and academic growth over time.

Theme 4: Content Summarization and Paraphrasing

Students across multiple national backgrounds consistently described generative AI as a practical and ethical support tool for navigating complex academic texts, particularly in fields such as business, management, and tourism, where synthesizing and interpreting dense source material is a core requirement. The ability to summarize lengthy or technical content allowed students to improve their comprehension and engage more efficiently with essential reading under time constraints. At the same time, paraphrasing functions were described as instrumental in promoting originality and avoiding academic misconduct, enabling students to restate ideas in their own words while preserving meaning. As one Cambodian participant (Cambodia 4) noted, “I used these applications whenever I need to digest a long reading or break down information quickly for assignments, especially when time is limited,” reflecting a utilitarian orientation toward time management and understanding. Similarly, a student from Myanmar (Myanmar 4) stated, “AI tools help me with paraphrasing; it’s easier to rewrite the content in my own words, which is important to avoid plagiarism,” reinforcing AI’s dual role as both a comprehension aid and an academic integrity safeguard. A Japanese respondent (Japan 4) echoed this, observing, “When there’s too much information, AI summarization is useful to pick out key points that I can use in my paper without directly copying,” indicating an awareness of the boundaries between inspiration and imitation.

Additional data from students outside the other countries further substantiates the theme. For instance, a student from China (China 2) shared, “Sometimes I use it to help me summarize and gather ideas,” suggesting that AI enables strategic simplification of content and supports ideation. Another student (Myanmar 9) reflected, “As I mentioned before, I paraphrased or fixed grammar using AI before submitting the assignments,” indicating that AI use often intersected with both language refinement and ethical self-monitoring. These reflections were not limited to practical benefits but extended to students’ internalization of academic norms regarding authorship and originality.

Interview data reinforced a critical ethical dimension not always visible in narrative responses. Several participants articulated a conscientious stance toward AI use, emphasizing its role in enhancing understanding rather than replacing independent work. For example, Interviewee 4 stated, “I read the answers ChatGPT gives but then rephrase them in my own way. I don’t copy anything directly,” demonstrating an intentional effort to preserve academic integrity. Likewise, Interviewee 5 acknowledged, “AI helps me with paraphrasing; it’s easier to rewrite complex content without copying, so I stay original,” affirming a disciplined use of AI as an interpretive rather than generative tool. Together, the data reveal that students not only valued AI for its functional utility but also viewed it as a mechanism for upholding ethical standards, suggesting a mature and reflective integration of generative tools into their academic routines.

What Challenges do International Business Students from ASEAN Countries in Thailand Encounter When Using Generative AI as a Toolkit in Their English Academic Writing?

International Students’ challenges of using GenAI in Academic Writing
Figure 2. International Students’ challenges of using GenAI in Academic Writing

As shown in Figure 2, four themes emerged on the international students’ challenges of using GenAi in academic writing, as explained below:

Theme 1: Reliance and Skill Impact

Students across the qualitative dataset expressed a shared concern regarding the potential negative impact of excessive dependence on generative AI, particularly in relation to the development of critical academic competencies such as analytical thinking and independent writing. Although generative AI was recognized for its usefulness in supporting idea generation and language enhancement, many participants feared that habitual reliance could gradually diminish their capacity to engage deeply with academic tasks. A Cambodian student (Cambodia 2) cautioned, “Using AI is good for education but relying on it too much could harm our skills in writing and thinking critically,” revealing apprehension about the erosion of foundational cognitive processes. Similarly, a Japanese respondent (Japan 1) noted, “Students may become dependent on AI and lose their critical thinking abilities,” while a participant from Myanmar (Myanmar 2) emphasized the importance of personal effort: “AI can be helpful, but we still need to try on our own to improve our skills and not lose our abilities.” These reflections demonstrate a widespread belief that uncritical use of AI might short-circuit the intellectual rigor required for meaningful learning.

Contributing further to this theme, a student from China (China 1) remarked, “Some of them maybe use it too much and become lazy,” drawing attention to behavioral shifts that may accompany overuse, such as reduced motivation or passive learning habits. In a related statement, another respondent (Cambodia 1) highlighted concerns that “students should not depend on it too much or they will stop thinking for themselves,” articulating a perceived link between AI dependency and cognitive disengagement. These insights suggest that students are not only aware of the instrumental value of AI but are also actively negotiating its boundaries, attempting to strike a balance between assistance and autonomy.

Interview data supported these themes by offering concrete examples of how excessive reliance on AI sometimes led to dissatisfaction with content relevance and quality. Students reported that AI-generated outputs were often too generalized or misaligned with the specific demands of academic assignments. Interviewee 3 explained, “Sometimes, the AI application puts out general information that doesn’t fit my topic. It’s hard to understand or get the specific information I need,” revealing the limitations of generic content in specialized academic contexts. Interviewee 5 similarly noted, “If students just trust what AI generates without checking, it might mislead them because it’s not always accurate,” indicating a growing recognition of the need for verification and critical oversight when using AI-generated materials. Collectively, the data reflect a cautious yet informed stance toward AI adoption—where students value its practical support but remain wary of its potential to undermine independent learning if used uncritically.

Theme 2: Ethical Concerns

Students’ reflections revealed deep ethical ambivalence surrounding the use of generative AI in academic contexts, particularly in relation to questions of fairness, authenticity, and academic integrity. Although many acknowledged the functional benefits of AI-assisted writing, they expressed discomfort with what they perceived as an erosion of effort-based achievement when peers used AI to complete tasks with minimal intellectual engagement. A student from Myanmar (Myanmar 1) articulated this concern, stating, “It’s unfair to compete with people who just let AI write their work without any effort. That’s not real learning,” highlighting a perceived imbalance between those who use AI responsibly and those who exploit it. Similarly, a Cambodian participant (Cambodia 1) reflected, “The challenges I think of could be students should not rely too much on AI… it can feel like cheating,” pointing to the moral tension students experience when AI becomes a substitute rather than a support. Another Cambodian student (Cambodia 2) echoed this anxiety, observing, “Sometimes it feels like a shortcut rather than actually learning and putting effort,” suggesting that the tool’s convenience may conflict with values of academic rigor and personal growth.

Importantly, the qualitative dataset also revealed concerns from underrepresented voices. For instance, a student from China (China 2) remarked, “Some people use it for copy…,” expressing apprehension about potential plagiarism and signaling a growing awareness of the ethical risks associated with AI-generated content. Another student from Cambodia (Cambodia 3) noted, “The challenge is that students probably use online tools without knowing the rules,” illustrating the need for clearer institutional guidelines. Myanmar 8 further cautioned, “Can be plagiarized and get 0 in some cases but I use it for idea only,” blending humor with a real concern about the consequences of misuse. These remarks reflect a broader cross-cultural recognition of the ethical complexities embedded in AI usage, extending beyond the original focus on a few national groups.

Interview data added depth to these ethical concerns by exposing students’ internal struggles to resist the temptation to copy highly polished AI outputs. Interviewee 2 described the difficulty of maintaining academic integrity when presented with seemingly perfect responses, stating, “The perfect essay you can see on AI—it’s hard not to just copy it. I have to constantly control myself not to copy.” Such candid admissions illuminate the psychological burden students carry when trying to uphold honesty in environments where AI can easily blur the boundaries between support and substitution. Similarly, Interviewee 1 warned, “There’s a risk of plagiarism… AI can produce extremely professional work, which can make it obvious when someone hasn’t done it on their own,” drawing attention to how AI-generated fluency might expose inconsistencies in student writing. The findings point to a growing tension between the pedagogical potential of generative AI and the risks it poses to ethical learning practices. Students are not only aware of these dilemmas but are actively seeking clarity on acceptable boundaries. Their repeated calls for institutional guidance reflect a desire to align AI usage with academic integrity, suggesting that educational institutions must engage proactively in shaping ethical AI literacies that preserve the value of original thought and individual effort.

Theme 3: Language and Interpretation

Students reported considerable challenges with the contextual accuracy of AI-generated content, particularly in relation to linguistic clarity and cultural interpretation within international academic environments. Although generative AI applications are often effective at producing grammatically correct output, students indicated that such content frequently lacked alignment with the nuanced meanings, disciplinary tone, and cultural specificity required in academic writing. The difficulties described by non-native English speakers reflected their awareness of the limitations inherent in AI’s monolingual and monocultural design, which often failed to accommodate the diverse linguistic repertoires and communication preferences found in multilingual classrooms. One student from Myanmar (Myanmar 1) explained, “The challenge is that students probably use only English input, and AI can sometimes misunderstand what we mean because it’s not tailored,” revealing how generic responses often fall short of contextual demands. Similarly, a Cambodian participant (Cambodia 1) noted, “It’s hard when AI doesn’t understand the specific meaning I need and gives a general response,” emphasizing the disconnection between user intention and AI output.

Voices from additional countries reinforced the concern that AI systems often misinterpret user prompts or return culturally misaligned answers. A student from Myanmar (Myanmar 16) reflected, “May be lack of understanding because of replying different way,” suggesting that AI-generated responses occasionally introduce confusion due to their limited contextual sensitivity. A South Korean participant (South Korea 1) highlighted the issue more broadly, stating, “AI can provide personalized assignment by analyzing the writing pattern, but sometimes it doesn’t reflect cultural or educational background,” pointing to the inadequacy of current models in addressing context-specific expectations of tone, genre, and academic expression. These remarks illustrate how generative AI’s one-size-fits-all design may inadvertently marginalize non-Western academic conventions, requiring students to engage in significant revision to align output with disciplinary norms.

Interview responses further revealed that even when AI-generated text was syntactically correct, it often lacked the precision and rhetorical appropriateness expected in formal academic contexts. As one interviewee explained, “The AI-generated text doesn’t always fit the exact meaning I’m looking for, so I have to adjust it to fit my academic context” (Interviewee 2). Such comments point to a broader trend in which students use AI as a draft-generating mechanism rather than a source of final output, acknowledging its limitations in capturing intended nuance and voice. The qualitative data emphasize that while AI can support surface-level language production, its inability to fully grasp user intent and cultural context often necessitates a high degree of post-editing—thus emphasizing the ongoing importance of linguistic awareness, academic literacy, and cultural sensitivity in AI-assisted writing.

Theme 4: Information Accuracy

Students’ reflections revealed persistent skepticism regarding the accuracy of AI-generated content, foregrounding a critical tension between AI’s utility and its epistemic reliability. Although generative AI offered a rapid means of retrieving information, participants emphasized that it frequently produced content that was either factually incorrect or overly generalized, thereby requiring users to adopt a cautious and evaluative stance. A student from Japan (Japan 1) remarked, “It’s difficult to check whether that information is accurate or reliable, especially for academic writing,” pointing to the additional burden of verification that undermines AI’s supposed efficiency. Echoing this concern, a Cambodian student (Cambodia 2) noted, “Sometimes the information generated is misleading, and it’s challenging to differentiate fact from fiction,” illustrating how the ambiguity of AI outputs complicates students’ decision-making processes when integrating content into formal assignments.

Additional responses from a wider demographic reinforced these concerns. A student from Myanmar (Myanmar 9) acknowledged, “False information. AI is a learnt tool, providing content that is not always true,” revealing an awareness of the algorithmic basis of AI and its potential to propagate inaccuracies. Meanwhile, a Russian participant (Russia 1) offered a contrasting view, stating, “I don’t think that there are any problems with using it. You just need to know how to do it right,” suggesting that critical literacy, rather than the tool itself, determines the reliability of AI integration. This perspective introduces an important nuance: students who approach AI with evaluative skills tend to mitigate its risks more effectively, while those who lack such skills may inadvertently incorporate misinformation.

Interview findings provided further insight into how overreliance on AI not only compromises informational accuracy but also impairs cognitive independence. Several students described academic situations, such as spontaneous presentations, where dependence on AI impeded their ability to think creatively or respond adaptively. As Interviewee 1 explained, “When we have to give presentations on the spot… if students rely too much on these websites, they might not have a creative idea on their own,” highlighting how habitual reliance can diminish students’ intellectual autonomy. Interviewee 5 similarly observed, “It’s hard to come up with unique ideas ourselves because we depend on AI so much… AI might replace our need to think critically or creatively,” emphasizing the long-term cognitive consequences of dependence. Taken together, the findings illustrate a shared sense of epistemic responsibility among students, who increasingly recognize that the benefits of generative AI are contingent upon their ability to critically assess and refine its outputs. Rather than positioning AI as a definitive source of truth, participants viewed it as a preliminary tool that demands additional scrutiny, thereby reinforcing the importance of digital literacy and fact-checking in the academic use of AI.

Discussion and Implications

Generative AI in Reducing Cognitive Load and Enhancing Research Focus

The study reveals that generative AI substantially facilitates academic research processes among international business students by streamlining data acquisition and synthesis. This finding is consistent with prior research (Marchandot et al., 2023; Noy & Zhang, 2023), yet the theoretical foundation underpinning this alignment warrants deeper analysis. Cognitive Load Theory (Sweller, 2011) provides a pertinent framework to interpret this outcome; by automating mechanical and repetitive tasks such as summarization and data retrieval, AI reduces extraneous cognitive load. Consequently, learners are better positioned to engage with germane cognitive processes, including critical interpretation and complex problem-solving. In this context, generative AI does not merely function as a tool of convenience but serves as an instrument of cognitive redistribution, shifting mental resources toward higher-order thinking. The parallel with Sasaki’s (2023) findings becomes theoretically significant when interpreted through this lens, as AI-supported cognitive offloading appears to foster students’ engagement with analytical tasks—a core demand in disciplines such as international business management.

Balancing Efficiency and Depth in Academic Engagement

Despite these benefits, concerns regarding superficial engagement with AI-generated content merit critical examination. The rapid access to structured information, although advantageous, may inadvertently discourage deep reading and reflective processing, echoing the warnings raised by Chan and Hu (2023) and Wang (2024). From a socio-cognitive perspective, the tension between efficiency and depth can be understood through the lens of dual-processing theory, wherein reliance on AI encourages heuristic rather than systematic processing. In contexts where academic rigor demands nuanced understanding and contextual adaptability, especially within non-Western educational environments, the homogeneity of AI-generated outputs risks decontextualizing learning. Khalifa and Albadawy’s (2024) observations highlight this issue, suggesting that AI’s lack of sensitivity to local cultural and epistemological paradigms may render its contributions less pedagogically relevant. Therefore, the study’s findings do not simply echo prior results; they foreground an emerging theoretical dilemma between automation and authenticity in knowledge construction.

Generative AI as a Cognitive Scaffold in Idea Formation

A further dimension of the study illustrates AI’s role in facilitating ideation and preliminary organization of academic content. Prior studies (Malik et al., 2023; Rad et al., 2023) have documented similar patterns, particularly in contexts involving language learners and cognitively demanding topics. The present study extends this discussion by framing AI’s contribution through Vygotsky’s concept of the Zone of Proximal Development (ZPD), whereby AI can be viewed as a semiotic tool that scaffolds the learner’s movement from assisted to autonomous performance. For international students navigating complex academic tasks, generative AI not only alleviates the initial inertia of idea generation but also models discursive patterns that may otherwise be inaccessible due to linguistic constraints. Nevertheless, caution is necessary, as overreliance on such scaffolds may hinder the internalization of cognitive strategies, thereby weakening long-term intellectual independence. The contrast with Farrelly and Baker’s (2023) argument thus becomes theoretically grounded: AI may support strategic thinking in the short term, yet its excessive use could impede the development of independent ideational competence, as warned by Sasaki (2023).

Language Development, Structural Accuracy, and the Risk of Homogenization

In terms of linguistic support, the utilization of tools such as Grammarly and Quillbot emerges as a common practice among participants, primarily aimed at enhancing grammatical precision and structural clarity. Although this finding mirrors those of Rad et al. (2023) and Khalifa and Albadawy (2024), it also reflects a deeper pedagogical shift in how linguistic competence is acquired and mediated. From the perspective of sociocultural theory, AI applications function as mediational means that extend learners’ capabilities in their non-native language. Yet, as students progress academically, a discernible pattern of selective engagement with AI tools suggests an evolving metacognitive awareness of their own linguistic growth. This phenomenon aligns with Kuteeva and Andersson’s (2024) concern regarding linguistic homogenization, whereby excessive reliance on AI may lead to conformity in academic expression. The study’s findings thus contribute to this theoretical debate by illustrating that, although AI tools support transitional competence, the long-term goal remains the cultivation of a distinct academic voice—an outcome best achieved through balanced integration of technological aids.

Ethical Implications and the Limitations of AI Interpretation

The ethical dimension of generative AI use forms a crucial aspect of the study’s implications, particularly in relation to academic integrity and the interpretation of AI-generated content. Students’ concerns about shortcuts and fairness resonate with Song’s (2024) observations, but they also reflect a deeper ethical dissonance when viewed through the lens of virtue ethics in education. The ambiguity surrounding the boundaries between acceptable assistance and intellectual dishonesty underscores the necessity for clearly defined institutional frameworks, as advocated by Chiu (2024). Moreover, the limitations of AI detection technologies, as reported by Wee and Reimer (2023), complicate this ethical terrain by undermining the transparency of evaluative processes. Parallel to these integrity issues are the technical shortcomings of generative AI in handling language specificity and accuracy, particularly for non-native users. Students’ need to cross-verify content aligns with the cognitive demands of critical media literacy, suggesting that AI, in its current form, requires supplementary human intervention for optimal academic performance. This highlights the importance of culturally responsive AI tools, a need also emphasized by Kanont et al. (2024), if generative AI is to support rather than constrain educational equity and epistemological inclusivity.

Conclusion, Limitation, and Recommendation

This study demonstrates that generative AI serves as both a valuable resource and a complex challenge for international business students engaging in academic writing. On the one hand, AI applications support students by facilitating data retrieval, aiding idea generation, and improving linguistic accuracy, thereby alleviating some of the cognitive demands associated with writing in a non-native language. Nonetheless, the findings reveal substantial concerns about over-reliance on AI, which may erode critical thinking skills and reduce opportunities for independent intellectual engagement. Ethical issues also surface as students grapple with the balance between AI assistance and maintaining academic integrity, particularly in contexts where AI-generated content could easily substitute for original thought. Furthermore, limitations in AI’s adaptability to cultural and contextual nuances raise issues about its effectiveness in truly supporting diverse student populations. This study’s context-specific focus, limited to international business students in Thailand, suggests that further research should explore AI’s role in various disciplines and educational settings to gain a fuller understanding of its academic implications. Institutions are encouraged to establish clear guidelines and educational programs that foster responsible AI use, helping students leverage these tools as supportive aids rather than replacements for essential academic skills.

About the Authors

Benjamin Panmei is a published author and lecturer in the Business English Program at Bangkok University International (Language and Culture for Business Department), Thailand. He has over 13 years of experience teaching various aspects of the English language—specializing in academic writing, reading, listening, speaking, and grammar—to both Thai and international undergraduate and graduate students. He holds a master’s degree in Curriculum Development and Instruction from Spicer Adventist University, India. His research interests include language, education, teaching, and technology. ORCID ID: 0000-0002-2788-5385

Yaruingam Phungshok Shimray completed a Master of Arts (M.A) in Education from Andrews University, USA, in 2002. Bachelor of Arts (B.A) in English Spicer Memorial College Pune, India 2000. Affiliated to Faculty of Liberal Arts, Prince of Songkla University Thailand since 2010, specializes in Linguistics and Languages. Research focuses on enhancing English communication skills, language competency, public speaking skills, reading skills among Thai EFL undergraduates. ORCID ID: 0009-0001-2599-7121

To Cite this Article

Panmei, B. & Shimray, Y. P. (2025). GenAI as a Toolkit for English Academic Writing Among International Business Students. Teaching English as a Second Language Electronic Journal (TESL-EJ), 29(2). https://doi.org/10.55593/ej.29114int

References

Alahdab, F. (2024). Potential impact of large language models on academic writing. BMJ evidence-based Medicine, 29(3), 201-202. https://doi.org/10.1136/bmjebm-2023-112429

Barrett, A., & Pack, A. (2023). Not quite eye to AI: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20(1), 1-24. https://doi.org/10.1186/s41239-023-00427-0

Cardon, P., Fleischmann, C., Logemann, M., Heidewald, J., Aritz, J., & Swartz, S. (2024). Competencies needed by business professionals in the AI age: Character and communication lead the way. Business and Professional Communication Quarterly, 87(2), 223-246. https://doi.org/10.1177/23294906231208166

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 1-18. https://doi.org/10.1186/s41239-023-00411-8

Chan, C. K. Y., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI, such as ChatGPT, in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 1-23. https://doi.org/10.1186/s40561-023-00269-3

Chiu, T. K. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 1-11. https://doi.org/10.1016/j.caeai.2023.100197

Clarke, V., & Braun, V. (2017). Thematic analysis. The journal of positive psychology, 12(3), 297-298. https://doi.org/10.1080/17439760.2016.1262613

Connelly, F. M., & Clandinin, D. J. (1990). Stories of experience and narrative inquiry. Educational Researcher, 19(5), 2-14. https://doi.org/10.3102/0013189X019005002

Courduff, J., & Moktari, A. (2022). Personal, cultural, and institutional perspectives of special education technology integrators: A narrative inquiry. Journal of Special Education Technology, 37(3), 413-425. https://doi.org/10.1177/01626434211019393

Daniels, J. (2008). Negotiating learning through stories: Mature women, VET and narrative inquiry. Australian Journal of Adult Learning, 48(1), 93-107. https://files.eric.ed.gov/fulltext/EJ797561.pdf

Essien, A., Bukoye, O. T., O’Dea, X., & Kremantzis, M. (2024). The influence of AI text generators on critical thinking skills in UK business schools. Studies in Higher Education, 1-18. https://doi.org/10.1080/03075079.2024.2316881

Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1-14. https://doi.org/10.3390/educsci13111109

Kang, J., & Yi, Y. (2023). Beyond ChatGPT: Multimodal generative AI for L2 writers. Journal of Second Language Writing, 62, 1-2. https://doi.org/10.1016/j.jslw.2023.101070

Kanont, K., Pingmuang, P., Simasathien, T., Wisnuwong, S., Wiwatsiripong, B., Poonpirome, K., Songkram, N., & Khlaisang, J. (2024). Generative-AI, a learning assistant? Factors influencing higher-ed students’ technology acceptance. Electronic Journal of e-Learning, 22(6), 18–33. https://doi.org/10.34190/ejel.22.6.3196

Kartal, G. (2024). The influence of ChatGPT on thinking skills and creativity of EFL student teachers: a narrative inquiry. Journal of Education for Teaching, 1-16. https://doi.org/10.1080/02607476.2024.2326502

Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 1-11. https://doi.org/10.1016/j.cmpbup.2024.100145

Khalil, F., & Pipa, G. (2022). Transforming the generative pretrained transformer into augmented business text writer. Journal of Big Data, 9(1), 1-21. https://doi.org/10.1186/s40537-022-00663-7

Kim, J., Yu, S., Detrick, R., & Li, N. (2024). Exploring students’ perspectives on Generative AI-assisted academic writing. Education and Information Technologies, 1-36. https://doi.org/10.1080/15391523.2024.2447727

Kohnke, L. (2024). Exploring EAP students’ perceptions of GenAI and traditional grammar-checking tools for language learning. Computers and Education: Artificial Intelligence, 7, 1-10. https://doi.org/10.1016/j.caeai.2024.100279

Kumar, S., Rao, P., Singhania, S., Verma, S., & Kheterpal, M. (2024). Will artificial intelligence drive the advancements in higher education? A tri-phased exploration. Technological Forecasting and Social Change, 201, 1-15. https://doi.org/10.1016/j.techfore.2024.123258

Kuteeva, M., & Andersson, M. (2024). Diversity and Standards in Writing for Publication in the Age of AI—Between a Rock and a Hard Place. Applied Linguistics, 45, 561-567. https://doi.org/10.1093/applin/amae025

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 1-4. https://doi.org/10.1016/j.patter.2023.100779

Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., & Darwis, A. (2023). Exploring artificial intelligence in academic essay: higher education student’s perspective. International Journal of Educational Research Open, 5, 1-11. https://doi.org/10.1016/j.ijedro.2023.100296

Marchandot, B., Matsushita, K., Carmona, A., Trimaille, A., & Morel, O. (2023). ChatGPT: the next frontier in academic writing for cardiologists or a pandora’s box of ethical dilemmas. European Heart Journal Open, 3(2), 1-3. https://doi.org/10.1093/ehjopen/oead007

Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 1-18. https://doi.org/10.1080/03075079.2024.2323593

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586

Ou, A. W., Stöhr, C., & Malmström, H. (2024). Academic communication with AI-powered language tools in higher education: From a post-humanist perspective. System, 121, 1-14. https://doi.org/10.1016/j.system.2024.103225

Rad, H. S., Alipour, R., & Jafarpour, A. (2023). Using artificial intelligence to foster students’ writing feedback literacy, engagement, and outcome: A case of Wordtune application. Interactive Learning Environments, 1-21. https://doi.org/10.1080/10494820.2023.2208170

Rhein, D. (2016). Westernisation and the Thai higher education system: Past and present. Journal of educational administration and history, 48(3), 261-274. https://doi.org/10.1080/00220620.2016.1184133

Sasaki, M. (2023). AI tools as affordances and contradictions for EFL writers: Emic perspectives and L1 use as a resource. Journal of Second Language Writing, 62, 1-2. https://doi.org/10.1016/j.jslw.2023.101068

Song, N. (2024). Higher education crisis: Academic misconduct with generative AI. Journal of Contingencies and Crisis Management, 32(1), e12532. https://doi.org/10.1111/1468-5973.12532

Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14, 1260843. https://doi.org/10.3389/fpsyg.2023.1260843

Sweller, J. (2011). Cognitive load theory. In Psychology of learning and motivation (Vol. 55, pp. 37-76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8

Teng, M. F. (2024a). A Systematic Review of ChatGPT for English as a Foreign Language Writing: Opportunities, Challenges, and Recommendations. International Journal of TESOL Studies, 6(3), 1-22. https://doi.org/10.58304/ijts.20240304

Teng, M. F. (2024b). “ChatGPT is the companion, not enemies”: EFL learners’ perceptions and experiences in using ChatGPT for feedback in writing. Computers and Education: Artificial Intelligence, 7, 100270. https://doi.org/10.1016/j.caeai.2024.100270

Thankdenchai, P., & Heesawat, C. (2016). The Thailand Qualification Framework: Similarities and differences in the measured and desired attributes. Journal of Supply Chain Management: Research and Practice, 10(2), 12-27. https://jscm.au.edu/index.php/jscm/article/view/132/95

Valcea, S., Hamdani, M. R., & Wang, S. (2024). Exploring the impact of ChatGPT on business school education: Prospects, boundaries, and paradoxes. Journal of Management Education, 48(5), 915-947. https://doi.org/10.1177/10525629241261313

Waluyo, B., & Kusumastuti, S. (2024). Generative AI in student English learning in Thai higher education: More engagement, better outcomes?. Social Sciences & Humanities Open, 10, 1-10. https://doi.org/10.1016/j.ssaho.2024.101146

Wang, C. (2024). Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers. Technology, Knowledge and Learning, 1-22. https://doi.org/10.1007/s10758-024-09744-3

Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13(11), 1-15. https://doi.org/10.3390/app13116716

Wee, H. B., & Reimer, J. D. (2023). Non-English academics face inequality via AI-generated essays and countermeasure tools. BioScience, 73(7), 476-478. https://doi.org/10.1093/biosci/biad034

Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1), 1-29. https://doi.org/10.1186/s41239-024-00453-6

Zaim, M., Arsyad, S., Waluyo, B., Ardi, H., Al Hafizh, M., Zakiyah, M., Syafitri, W., Nusi, A., & Hardiah, M. (2024). AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory. Computers and Education: Artificial Intelligence, 7, 100335. https://doi.org/10.1016/j.caeai.2024.100335

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