The particular dialect or language that a person chooses to use on any occasion is called a code 1605 Copyright © 2024 The Author IDEAS is licensed under CC-BY-SA 4.0 License Issued by English study program of IAIN Palopo IDEAS Journal of Language Teaching and Learning, Linguistics and Literature ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) Volume 13, Number 1, June 2025 pp. 1605 - 1621 Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers Isidorus Bagus Geovenda Nyudak1, Made Hery Santosa2, Ni Putu Era Marsakawati3, Putu Susrini4 1,2,3,4Ganesha Univeristy of Education, Bali Corresponding E-Mail: isidorus@undiksha.ac.id1* Received: 2025-06-13 Accepted: 2025-06-27 DOI: 10.24256/ideas. v13i1.7014 Abstract This research explored the readiness and concerns of junior high school EFL teachers in Badung Regency, Bali, Indonesia, regarding generative AI (GenAI)—an AI system that produces content such as texts, pictures, sounds, or lesson materials— in classroom instruction. Utilizing an explanatory sequential mixed-methods approach, the data were collected from 165 teachers through a readiness survey adapted from Aydin and Tasci’s model, followed by interviews with eight representatives using the Concerns-Based Adoption Model (CBAM). This research’s findings revealed that 87.3% of teachers considered themselves ready, especially in the Technology and Innovation dimensions, while institutional support remains the primary barrier. Teachers showed strong personal motivation but were constrained by limited training and funding. The interviews indicated progression beyond the awareness stage, with some representatives already in the advanced adoption stage. This research highlights the urgent need for differentiated training, inclusive policies, and ethical frameworks to support the sustainable and effective utilization of GenAI in education. Keywords: Generative AI; EFL teachers; readiness; concerns; English Language Teaching Introduction Artificial Intelligence (AI) has significantly impacted education by presenting many solutions and innovations that can increase the quality of the learning process. The global data reveals that more than 80% of educators currently utilize AI for various educational purposes. Meanwhile, AI adoption has grown rapidly during the post-pandemic. As the adoption of AI in education expands, a significant paradigm shift occurred in the way learning is designed and delivered. Currently, http://u.lipi.go.id/1457703302 mailto:isidorus@undiksha.ac.id1* Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1606 AI can help teachers increase learning process efficiency and provide personalized and adaptive learning experiences. By recognizing student learning patterns through automatic feedback, AI can adapt teaching materials to each individual’s needs (Bojorquez & Vega, 2023; Cardona et al., 2023; Harry, 2023; Kumar, 2023; Mures an, 2023; Neha, 2020; Rahiman & Kodikal, 2024). Therefore, this technology will address classic educational challenges, such as classroom management and learning evaluation. Although AI has immense potential in education, English for Foreign Language (EFL) teachers face distinct challenges. Previous related research indicates that while many teachers support the integration of technology in education, challenges such as limited technical skills, inadequate infrastructure, and resistance to innovation hinder its adoption (Crompton et al., 2022; Edmett et al., 2023). Pedagogical barriers also arise, as preliminary interviews reveal a lack of training for effective technology integration. Additionally, local needs must be taken into account. For instance, tourism-dependent regions like Badung, where more than 50% of the workforce relies on English proficiency for tourism (Dinas Pariwisata Badung, 2023), require communicative English teaching but lack AI- aligned curricula (Utami et al., 2021). Factors such as language standardization and ideological concerns further complicate the implementation. Thus, assessing teacher readiness is crucial to ensure AI and other technologies are effectively adopted to maximize English language teaching (ELT). The topic of EFL teachers’ readiness to integrate AI technology, especially GenAI in ELT, remains a relatively underexplored area of research in Indonesia, including in Bali, particularly in Badung Regency. Therefore, this study intends to fill in the gap with two research questions: 1. What are the EFL teachers’ Generative AI (GenAI) readiness levels in Badung Regency? 2. What are the stages of concern among EFL teachers in Badung Regency regarding Generative AI (GenAI) implementation in ELT? This study aims to explore teachers’ readiness levels and stages of concern regarding the integration of GenAI into their teaching practices. Given the lack of similar research in local contexts such as Badung and Bali, this study is expected to make a significant contribution to the development of academic literature and educational practice. The results of this study can be utilized by policymakers, educational institution leaders, and educational technology (EdTech) developers in designing appropriate and contextually relevant AI implementation strategies. Thus, this study not only broadens scientific insight but also makes a real contribution to encouraging technology-based learning transformation in Badung, Bali, Indonesia. IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 1607 Method This study will examine the readiness and concerns of junior high school EFL teachers regarding the integration of GenAI in ELT in Badung Regency, Bali, Indonesia. The approach employed is an explanatory mixed-methods design based on Creswell and Creswell’s (2018) model, which combines quantitative data collection at the initial stage with qualitative data collection. Figure 1. Explanatory Sequential Mixed-Method Design In the first phase, quantitative data was collected through a structured survey to measure teachers’ readiness to utilize GenAI. The survey includes questions on EFL teachers’ understanding of GenAI with four factors measured: The People Factor, Self-Development Factor, Technology, and Innovation. The survey was distributed to a large number of EFL teachers at the junior high school level in Badung Regency to obtain representative data. After the quantitative data were analyzed, the second phase involved conducting semi-structured interviews to explore teachers’ stages of concern regarding the integration of GenAI in ELT. Several teachers were selected based on the results of the survey. The interview aims to explore the concerns teachers have regarding the implementation of GenAI in ELT. Respondents and Sampling The target respondents were junior high school EFL teachers in Badung Regency. To obtain valid and representative data, this study targeted large-scale participation. Based on data from the English Teachers Forum and the SIMPKB platform, the total population of EFL teachers in Badung was recorded at 232 people. By using the Slovin formula, the expected number of respondents in this study is 148. Interview representatives were selected purposively based on their readiness level (very high = 3 representatives, high = 3 representatives, medium = 1 representative, low = 1 representative) to ensure varied perspectives. Quantitative Data Collection (Survey) Quantitative Data Analysis (Descriptive Statistics) Qualitative Data Collection (Semi-Structured Qualitative Data Analysis (Thematic Analysis) Data Interpretation and Integration Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1608 Instruments The questionnaire was designed to evaluate various aspects of teacher readiness in integrating GenAI in ELT. The instrument was adapted from Aydin and Tasci’s (2005) E-Learning Readiness Model and validated by two experts. It measures four dimensions: People, Self-Development, Technology, and Innovation. Cronbach’s alpha for internal consistency was > 0.80 across all dimensions. Table 1. Instrument Reliability Reliability Statistics Cronbach’s Alpha N of Items .806 27 The second instrument used in this study was a semi-structured interview guideline designed to explore teachers’ stages of concern in integrating GenAI. This interview will employ the Concerns-Based Adoption Model (CBAM) approach, as outlined by Hall (1974), which comprises seven stages: awareness, informational, personal, management, consequence, collaboration, and refocusing. The explanation of each stage of concern is described in the following table: Table 2. Stage of Concerns Stage Concerns Level Description 0 Awareness Not aware or not interested in the innovation. 1 Informational Seeking basic information about the innovation. 2 Personal Concerned about how the change affects oneself. 3 Management Focused on organizing and handling the change. 4 Consequence Concerned with the impact on students/outcomes. 5 Collaboration Interested in working with others to improve use. 6 Refocusing Thinking about changes or alternatives to improve it. The combination of quantitative and qualitative methods is expected to provide a comprehensive picture of teacher readiness and concerns regarding the application of GenAI in ELT. IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 1609 Data collection procedures The research employed a two-phase data collection process between March and June 2025. The initial survey phase (March-May 2025) utilized dual distribution methods to ensure robust participation. Researchers conducted in- person visits to schools, providing direct assistance to respondents as they completed the survey. Besides, personalized digital outreach was implemented through group messages on MGMP with official permission from the leader. The interview (May-June 2025) involved purposive sampling of EFL teachers representing varying levels of readiness toward GenAI adoption. Conducted on-site at schools, these 30–45-minute semi-structured interviews collected insights into teachers’ preparedness, concerns, and perceived challenges regarding GenAI in the English language classrooms of Badung. Data analysis To analyze the GenAI readiness level of EFL teachers, descriptive statistics were employed using SPSS v26 software. In this analysis, we focused on the readiness scale. Based on the Aydin and Tasci(2005) model, the readiness scale is divided into four categories: Table 3. Assessment Model of GenAI Readiness Mean Score Range Description 1 – 2.6 Not ready needs a lot of work 2.61 – 3.4 Not ready needs some work 3.41 – 4.2 Ready but needs a few improvements 4.21 – 5 Ready go ahead The results of this analysis will reveal the extent to which teachers are prepared in terms of technology, innovation, human resources, and self- development to adopt GenAI in ELT. To analyze EFL teachers’ concerns about GenAI, thematic analysis was utilized. Data analysis in this phase involved transcribing interviews, thoroughly reading and understanding the data, identifying key ideas or patterns, and pinpointing phrases, sentences, or narratives that indicate teachers’ stage of concern. Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1610 Results This section presents the findings and discussions of the two research problem formulations. The findings were obtained through two data collection techniques: surveys and semi-structured interviews, which covered aspects of the readiness and concerns of EFL teachers in Badung Regency regarding the implementation of GenAI in ELT. From a total population of 232 EFL teachers at the Junior High School in Badung Regency, 165 teachers participated in the survey. Based on gender, the majority of respondents were female, comprising 120 people (72.7%), while males numbered 45 people (27.3%). Figure 2. Gender Participation Based on school domicile, the largest number of participants came from Kuta Utara District (26.7%), followed by Mengwi (21.8%), Kuta Selatan (21.2%), Abiansemal (14.5%), Kuta (12.7%), and Petang (3%). Regarding educational background, most respondents had completed their education at the Bachelor’s level, with 90% having done so, and the remaining 10% had completed their Master’s degree. 45 120 Male Female IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 1611 Figure 3. School Domicile Regarding experience in participating in professional activities, 66.7% of teachers have participated in technology or AI-based seminars or workshops within the context of ELT. In comparison, 33.3% have not had such experience. In daily learning practices, the use of technology by teachers is classified as active, with the following details: 32.1% of teachers use technology daily, 48.5% use it weekly, 18.8% use it occasionally, and only 0.6% do not use it at all. Figure 4. Technology Utilization Frequency 26.70% 21.80% 21.20% 14.50% 12.70% 3% Kuta Utara Mengwi Kuta Selatan Abiansemal Kuta Petang 32.10% 48.50% 18.80% 0.60% Daily Weekly Occasionally Never Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1612 These findings provide a comprehensive initial picture of respondents’ demographic characteristics and their initial level of readiness to adopt GenAI in the educational context. The following description further elaborates on the findings related to each research objective. Teachers’ Generative AI Readiness Level From the total population of 232 EFL teachers in Badung Regency, 165 respondents participated in the survey. The results revealed varying levels of preparedness: 69 teachers (41.8%) were classified as “ready go ahead,” indicating confidence in adopting GenAI. Meanwhile, 75 respondents (45.5%) fell into the “ready but need a few improvements” category, suggesting the need for slight refinements in their skills or understanding. A smaller subset of 20 teachers (12.1%) were deemed “not ready need some work,” reflecting significant gaps in preparedness. Only one participant (0.6%) was categorized as “not ready needs a lot of work,” highlighting pronounced challenges in GenAI adoption. The distribution illustrates a spectrum of readiness levels, highlighting the need for targeted professional development to address competency disparities. Figure 5. Distribution of EFL Teachers’ GenAI Readiness Levels Besides, to analyze the factors influencing teachers’ readiness for GenAI integration, descriptive statistics were computed, including mean scores and standard deviations for each determinant. These metrics provide a quantitative assessment of the central tendencies and variability in respondents’ preparedness levels. The subsequent table presents the statistical breakdown for each factor, offering a detailed comparison of their relative influence. 69 75 20 1 Ready go ahead Ready but need a few improvements Not ready need some work Not ready needs a lot of work IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 1613 Table 4. Statistics for Each Factor Factor N M SD People Factor 165 3.37 1.009 Self-Development Factor 165 3.87 0.839 Technology Factor 165 4.44 0.710 Innovation 165 4.16 0.761 Overall 165 3.96 0.829 According to the E-Learning Readiness Model scale, the People Factor obtained a mean score of 3.37 (SD = 1.009), which falls into the category of “not ready, needs some work.” This dimension includes access to training departments, resource persons, experienced personnel, and external vendors who can support the implementation of GenAI in ELT. The relatively low mean score indicates that institutional and interpersonal support mechanisms for GenAI integration are not yet sufficiently established and require greater attention and more strategic management. In contrast, the Self-Development Factor, which evaluates individual and institutional efforts in promoting professional development related to GenAI, recorded a mean score of 3.87 (SD = 0.839), indicating that teachers are “ready but need a few improvements.” This finding suggests that EFL teachers in Badung Regency generally exhibit a positive attitude towards improving their professional competence through participation in workshops, seminars, or training sessions. Furthermore, they seem to be increasingly aware of GenAI’s strategic importance in strengthening their institutions’ quality and reputation. The Technology factor obtained the highest mean score of 4.44 (SD = 0.710), which is categorized as “ready, go ahead.” This factor encompasses teachers’ access to digital infrastructure, proficiency in basic computer and internet skills, and their readiness to integrate technology into their teaching practices. These results indicate that technology readiness is a significant strength among teachers, indicating that technical barriers are minimal in the context of GenAI adoption. Meanwhile, the Innovation Factor, which captures teachers’ openness to change and barriers to innovation, recorded a mean score of 4.16 (SD = 0.761), classified as “ready, but needs a few improvements.” Respondents generally indicated a willingness to embrace change, such as moving to digital lesson planning or adopting new pedagogical strategies. However, minor concerns remained regarding potential internal or external challenges, such as political, legal, or cultural barriers, that could impact innovation efforts. While there is an encouraging level of readiness regarding technology adoption and openness to innovation, ongoing and continuous development remains essential to ensure that GenAI integration into ELT is implemented and managed effectively. Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1614 Teachers’ Generative AI Concerns After analyzing the quantitative data in the first phase, the research proceeded to the second phase. In the second phase, semi-structured interviews were conducted to delve deeper into the stage of concerns from the representatives. In selecting the interview representatives, a purposive sampling technique was utilized. Four respondents with very high GenAI acceptance levels were invited, and three of them agreed to participate in the interview. Three respondents with high levels of GenAI acceptance were invited to the interview, and all agreed. In the medium GenAI acceptance levels, three prospective representatives were invited, but only one of them was open for the interview. For the representatives of low levels of GenAI acceptance, one representative was invited and agreed to participate. Based on the thematic analysis related to the Concerns- Based Adoption Model (CBAM), this research revealed the adoption patterns of GenAI among EFL teachers in Badung Regency. The majority of representatives (7 out of 8) exceeded the “Awareness” stage (Stage 0) and were in either the “Informational” stage (Stage 1) or the “Personal” stage (Stage 2). As stated by the fourth interviewee: “At first, I was amazed and curious... but after understanding GenAI’s functions, I became interested in it.” However, at this stage, there were ethical-pedagogical concerns encountered by the representatives, especially the risk of students’ dependency on GenAI, as stated by the third interviewee: “I am definitely worried... I am afraid students will not be able to think for themselves since they will become dependent on GenAI to complete their tasks and assignments.” Five out of eight representatives (the first, the second, the third, the fourth, and the fifth representatives) had reached the “Management” stage with active implementation (Stage 3) by utilizing GenAI regularly to prepare learning materials and conduct learning evaluations. As stated by the third interviewee: “Before each class, I routinely use ChatGPT to generate brainstorming questions and creative activity prompts.” Some of them had reached the “Consequence” stage (Stage 4), in which they considered GenAI utilization to be one of the best options for time efficiency and student engagement, as stated by the first interviewee: IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 1615 “Student engagement has increased significantly since we incorporated interactive GenAI tools into our lessons.” There were some interesting findings, in which some of the interviewees showed characteristics of the “Collaboration” stage (Stage 5) and “Refocusing” stage (Stage 6) through active discussion on teachers’ forum (MGMP) and strategic vision development of GenAI utilization as emphasized by the fifth interviewee: “Ideally, GenAI should become teachers’ partner, not a replacement.” However, two of the interviewees (the seventh and the eighth interviewees) were still in the adoption stage. The seventh interviewee revealed: “I have never tried GenAI or similar tools. I usually use Canva.” This contrast shows that GenAI adoption is uneven, even among tech-aware teachers. It emphasizes the importance of differentiated training and inclusive policies. Besides, teachers at advanced levels could become mentors, while institutional regulations should support digital, innovative exploration inclusively, as suggested by the sixth interviewee: “The regulations... should allow teachers to seek learning resources from anywhere, including GenAI.” Findings from both phases intersect meaningfully. Teachers with high scores in the Technology and Innovation Factors were also those most likely to reach the Management and Collaboration stages in CBAM. However, high self-development scores did not always translate to GenAI use, reinforcing the role of external support systems. The implications of this research suggest that the adoption of GenAI requires gradual and sustained guidance and support. Teachers with advanced levels of GenAI utilization can become agents of change. At the same time, institutional policies need to facilitate the transition that considers various levels of GenAI readiness, encompassing not only technical aspects but also pedagogical and ethical dimensions. Discussion The People Factor yielded a mean score of 3.37 (SD = 1.009), indicating that the EFL teachers were categorized as low in terms of institutional and interpersonal support. The mean score suggests that the teachers may not have received adequate professional, guided formal training and vendor support. The lack of collaboration with external vendors and GenAI training programs means that teachers often learn independently, which does not always provide a Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1616 consistent and holistic understanding (Aydin & Tasci, 2005; Cardona et al., 2023). This contrasts sharply with Singapore’s systematic AI training initiatives (MOE Singapore, 2023) and highlights structural gaps in Indonesia’s decentralized education system. Additionally, the low mean score on this factor suggests that the presence of internal GenAI mentors or facilitators in schools remains limited. Without adequate policies that provide sustainable training programs, strategic collaboration with technology experts, and professional practice development communities, the effort of GenAI integration might be infrequent and unsustainable (Bojorquez & Vega, 2023; Choukaier, 2024; Kumar, 2023; Moura et al., 2024). Educational institutions and regional governments should initiate a systematic approach by providing specific training, field-based modules, and interschool forums for sharing best practices in GenAI for ELT. The Self-Development Factor showed relatively high readiness (M=3.87, SD=0.839), indicating strong intrinsic motivation among EFL teachers to adopt GenAI for designing learning activities and providing personalized feedback. Notably, personal motivation becomes the primary influencing factor in adopting technological innovations. Furthermore, interviews revealed that teachers viewed GenAI as valuable for improving learning quality and institutional reputation. However, structural barriers, such as budget constraints and insufficient institutional training support, created a “motivation-resource gap” (Foley et al., 2025; Yue et al., 2024), limiting the translation of motivation into consistent practice. Consequently, besides providing incentives, allocating space and time for innovative technology exploration and establishing teachers’ learning communities become essential to fostering curiosity and sustainable digital skills development (Bojorquez & Vega, 2023). The Technology Factor showed the highest mean score of 4.44 (SD = 0.710), indicating that EFL teachers generally have proper access to hardware, software, internet connections, and digital literacy skills. Indeed, the high mean score indicates that technology is not a serious barrier to GenAI adoption processes. This finding is consistent, as the global trend has shifted significantly in terms of technology utilization since the onset of the post-pandemic era. However, there is an indication that institutional investment is inadequate to ensure the sustainability of infrastructure, particularly in areas such as device maintenance, periodic upgrades, and equitable facility distribution among schools (Jiang et al., 2023). Therefore, in this context, education authorities and school leaders need to formulate a strategic and sustainable infrastructure management plan. Specifically, technical support at schools, periodic hardware audits, and responsive technical issue management will help stakeholders ensure that the utilization and adoption of GenAI run smoothly (Choukaier, 2024; Sing Yun, 2023). IDEAS, Vol. 13, No. 1, June 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 1617 Finally, the Innovation Factor reflects the high readiness of EFL teachers for change, with a mean score of 4.16 (SD = 0.761). Notably, this is supported by the statement given by the teachers, who say that there are no political or cultural barriers to adopting GenAI. Additionally, these findings revealed that, psychologically and socioculturally, EFL teachers in Badung Regency have relatively open ecosystems for experimenting with and adopting technology-based new methods (Awashreh & Hamid, 2025; Peruzzo et al., 2023). Indeed, this kind of readiness is a primary principle for the success of GenAI integration. However, this potential needs to be facilitated by policies that encourage innovation cultures, clarify regulations related to GenAI utilization in classrooms, and provide forums for collaboration among teachers. Consequently, stakeholder collaboration, including EdTech developers, teachers, school leaders, and policymakers, needs to be orchestrated to realize transformative and sustainable learning practices (Arstorp, 2024; Riza et al., 2025). Building upon the quantitative data, based on the CBAM, the findings revealed a stratified adoption landscape, where 62.5% of interviewed teachers had reached advanced implementation stages (Stages 3–6), while 25% remained in early stages (Stages 0–2). Notably, several key points require emphasis. Specifically, two of the interviewed teachers have not had direct experience with GenAI utilization. Moreover, Bali’s collectivist professional culture significantly impacted adoption patterns, as teachers in advanced stages all actively participated in MGMP teacher working groups. This finding consequently highlights the importance of developing differential and adjusted training programs tailored to teachers’ competencies and self-confidence (Crompton & Burke, 2023; Stovner & Klette, 2022). Taken together, both research phases —the quantitative and the qualitative —showed that EFL teachers in Badung Regency are generally motivated and technically equipped to adopt GenAI. However, their motivation and readiness need to be supported by systemic support, policies, resources, training, and a clear framework to tackle possible barriers, such as inconsistency in utilization and ethical considerations (Bojorquez & Vega, 2023; Choukaier, 2024; Kumar, 2023; Sing Yun, 2023). Therefore, based on these points, a professional development program should not only focus on device utilization but also emphasize the ethical and responsible use of technology in an educational context. This research reveals that Badung’s GenAI adoption challenges stem not from technological barriers but from mismatches between teacher motivation and systemic support. This suggests that Indonesia’s policy focus should shift from infrastructure provision to developing context-sensitive support structures that leverage cultural assets, such as MGMP networks. Future research should investigate longitudinal learning outcomes and compare urban-rural adoption patterns across Indonesia while also addressing the ethical considerations unique to AI-generated content in educational settings. Isidorus Bagus Geovenda Nyudak, Made Hery Santosa, Ni Putu Era Marsakawati, Putu Susrini Generative AI in ELT: Readiness and Concerns of Badung EFL Teachers 1618 Conclusion This study examines the readiness and concerns of EFL teachers in Badung Regency regarding the integration of GenAI in ELT. This research found that most teachers are open and ready to adopt GenAI, especially in terms of technological access and openness to innovation. However, institutional support and training are still limited, as reflected in the People Factor score, which is relatively low and requires improvement. The EFL teachers generally accept GenAI, as indicated by the qualitative findings based on CBAM, which revealed various stages of adoption. Most teachers are actively utilizing GenAI and collaborating with the forums during implementation. However, few interviewed teachers are resistant to GenAI adoption, as they have less experience and exposure to it. Generally, successful GenAI integration requires not only motivated users but also proper institutional support, training, and clear ethical viewpoints. With adequate supporting systems, GenAI could become a powerful tool for improving ELT practices sustainably and contextually. Acknowledgement The author wishes to convey sincere appreciation to all individuals and institutions who have contributed to the successful completion of this research. Special acknowledgment is extended to colleagues for their valuable support and assistance throughout the research process. The author is deeply grateful to the academic supervisor for their insightful guidance, constructive feedback, and continuous encouragement. 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