Rural Secondary English Teachers' Journeys With AI: Contextual Factors, Challenges, and Opportunities

International Journal of Social Science and Human Research ISSN (print): 2644-0679, ISSN (online): 2644-0695 Volume 08 Issue 12 December 2025 DOI: 10.47191/ijsshr/v8-i12-51, Impact factor8.007 Page No: 9593-9601 IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9593 Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities Zarah May C. Batulat1, Marijo D. Chua2 1,2Carlos Hilado Memorial State University, Talisay City, Negros Occidental Philippines ABSTRACT: With Artificial Intelligence becoming increasingly prominent in education worldwide, it is essential to understand how rural educators experience, adapt to, and negotiate the use of these technologies to promote equitable learning outcomes. Using a qualitative phenomenological design guided by Moustakas’ transcendental phenomenological framework, this study explored the lived experiences of English teachers in rural secondary schools in the Central Philippines as they used AI tools in curriculum design, assessment, and classroom instruction. The data were collected through in-depth interviews with four purposefully selected English teachers who utilize AI platforms such as ChatGPT, Copilot, and Grammarly. Findings revealed six major themes such as Enhancement of Pedagogical Competence, Provision of Personalized Learning Pathways, Limitations of AI Utilization, Adherence to Ethical Standards, Elevating Assessments and Enhancing Engagement. Findings underscored the imperative to improve digital infrastructure, provide context-specific professional development, and establish clear ethical frameworks to optimize the benefits of AI in resource-constrained rural schools. This study contributes to policy formation, school leadership strategies, and curriculum development aimed at fostering equitable and effective English education in the AI era. KEYWORDS: artificial intelligence, rural educators, English teachers, phenomenology INTRODUCTION The rapid advancement of artificial intelligence (AI) has revolutionized various sectors globally, including education, where it is increasingly recognized as a transformative tool for enhancing teaching and learning processes. AI-powered platforms such as ChatGPT, Copilot, and Grammarly offer educators opportunities to design instructional materials more efficiently, streamline assessment procedures, and support personalized learning pathways (Luckin et al., 2016; Aljanabi, 2023). These technologies offer notable advantages such as increased efficiency, reduced workload, and enhanced learner engagement while simultaneously raising concerns regarding academic integrity, accuracy, and ethical use (Holmes et al., 2021; Floridi & Cowls, 2019). Consequently, the integration of AI in education represents a balance between innovation and caution, requiring educators to exercise critical judgment to ensure that technology enhances rather than replaces pedagogical expertise. Globally, AI in education is positioned as a catalyst for innovation that addresses persistent challenges such as teacher workload, student engagement, and differentiated instruction, as emphasized by the Organization for Economic Co-operation and Development ([OECD], 2021). Despite these prospects, integrating AI remains challenging, particularly in rural contexts where infrastructural deficits, restricted access to technology, and low digital literacy constrain effective utilization (Sharma & Singh, 2023; Alam, 2022). Moreover, the rapid deployment of AI raises ethical dilemmas related to academic integrity, privacy, and potential over reliance, while simultaneously opening new avenues for pedagogical innovation and efficiency (Dwivedi et al., 2021; Holmes et al., 2019). Within the Philippine educational landscape, the Department of Education (DepEd) has consistently prioritized technology integration to promote equitable and inclusive education, aligning efforts with global frameworks such as Education 2030 and the United Nations Sustainable Development Goals (SDGs). While AI holds promise for enhancing teaching efficiency and learnercentered pedagogy, its adoption in rural secondary schools faces significant barriers, including inadequate internet connectivity, insufficient technological infrastructure, and inequitable access to digital resources (Department of Education, 2023; Alam, 2022). English teachers in rural secondary schools encounter unique complexities in this regard. As English remains a core subject and a commonly used medium of instruction, it demands high linguistic proficiency, comprehension, and contextualized content. However, rural students often experience limited exposure to English, diminished study habits, and scarce learning materials, limiting their achievement in communication and literacy skills (Reyes & Santos, 2020). To address these challenges, teachers frequently adopt adaptive pedagogical strategies, simplifying competencies, contextualizing lessons to rural life, and employing experiential, student-centered approaches to sustain engagement and improve learning outcomes (Alcantara & Velasco, 2021). AI technologies adds both opportunities and complexities to this setting. Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9594 AI’s applications in curriculum design and lesson preparation are well documented. For instance, Li and Ni (2021) argue that AI can transform broad learning goals into manageable instructional strategies, thus reducing lesson planning workload. In rural schools, this contextualization is critical, as teachers must adapt AI-generated materials to resonate with agricultural and communitybased realities (Sharma & Singh, 2023). Notwithstanding these advantages, systemic barriers frequently inhibit effective AI utilization, especially in under-resourced settings. Alam (2022) identifies inadequate infrastructure such as unreliable internet and device scarcity as a central obstacle in developing regions, while Warschauer and Matuchniak (2010) highlight the enduring digital divide that exacerbates educational inequities. Moreover, AI-generated content may lack the precision and cultural sensitivity necessary for the nuanced rural context, necessitating prompt and critical teacher intervention (Dwivedi et al., 2021). These study reveals six interconnected themes that encapsulate the lived experiences of rural English teachers in utilizing AI: Enhancement of Pedagogical Competence, Provision of Personalized Learning Pathways, Limitations of AI Utilization, Adherence to Ethical Standards, Elevating Assessments, and Enhancing Engagement. Through Enhancement of Pedagogical Competence and Elevating Assessments, teachers employed AI as a pedagogical aid to streamline lesson design and assessment creation, emphasizing the need for validation and contextual refinement of AI outputs. Limiting Challenges reflects the infrastructural, technical, and pedagogical constraints encountered by teachers, while Adherence to Ethical Standards underscores their moral responsibility in fostering academic honesty and responsible AI usage. Meanwhile, Engaging Opportunities highlights the perceived benefits of AI in enhancing instructional efficiency and learner participation, and Provision of Personalized Learning Pathways illustrates teachers’ adaptability in integrating AI tools that align with the socio-cultural realities of rural learners. Collectively, these themes illuminate the complex and evolving relationship between technology and pedagogy in rural educational contexts. While AI offers avenues for instructional innovation, its successful integration depends largely on teachers’ capacity to interpret, adapt, and contextualize digital tools within the realities of their classrooms. Despite the growing discourse on AI in education, limited research has examined how rural English teachers in the Philippines experience and negotiate its implementation amid contextual constraints. Addressing this research gap, the present study, grounded in Moustakas’ (1994) transcendental phenomenological framework, explores the lived experiences of rural secondary English teachers in utilizing AI. By foregrounding their narratives, this research seeks to provide insights that inform context-sensitive, ethically grounded, and pedagogically responsive approaches to AI integration in rural English education. The main objective of the study was to explore the lived experiences of English teachers in rural secondary schools towards the utilization of Artificial Intelligence. Specifically, this study sought to answer the following questions: What contextual factors influence rural secondary English teachers in using AI? What are the challenges do rural secondary English teachers face in using AI? What opportunities do rural secondary English teachers perceive in using AI? METHODOLOGY Approach to the Qualitative Research This study employed a qualitative phenomenological research design grounded in Moustakas’ (1994) transcendental phenomenology. Rooted in Husserl’s (1970) philosophical foundation, this approach seeks to uncover and describe the essence of individuals’ lived experiences of a particular phenomenon. The process of transcendental phenomenology involves setting aside researcher assumptions through epoche, identifying significant statements, formulating meanings, clustering these meanings into themes, and synthesizing them to reveal the core essence of the experience (Creswell, 2013). This reflective and participant-centered approach provides a systematic yet flexible means of understanding human experiences as they are lived, rather than as they are conceptualized or theorized. The phenomenological approach is particularly suited to exploring educational experiences in underrepresented contexts. Scholars such as van Manen (1990) and Smith, Flowers, and Larkin (2009) emphasize that phenomenology captures the richness and complexity of human meaning-making. Accordingly, this study adopts phenomenology to provide an in-depth and contextualized understanding of how rural secondary English teachers experience and interpret AI utilization in their instructional practices, uncovering both the challenges and the transformative opportunities. Conversation Partners This study gathered qualitative data through in-depth interviews conducted with four English teachers employed in rural secondary schools in the Central Philippines who met specific inclusion criteria: at least two years of teaching experience in rural secondary schools, prior utilization of AI tools in instructional planning or classroom implementation, and participation in AI-related professional development or training programs. These criteria ensured that the participants possessed sufficient experience, technological exposure, and pedagogical insight to provide rich and meaningful reflections on the utilization of AI in English language teaching within rural contexts. Each participant contributed nuanced perspectives that reflected both the affordances and challenges of integrating AI in under-resourced educational environments. To preserve confidentiality and enhance data integrity, pseudonyms were assigned to each participant to maintain anonymity while facilitating coherent narrative presentation. Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9595 The gatekeepers for the study were the principals of the participating rural secondary schools. Their role encompassed granting access to the schools and facilitating participant recruitment. Gatekeeper cooperation was essential for ensuring ethical permissions, fostering a trusting environment conducive to candid discourse on AI utilization, and assisting in identifying eligible participants. Despite their facilitative role, the researchers maintains autonomy by directly inviting teachers to participate, obtaining informed consent, and rigorously safeguarding voluntary participation and data confidentiality. The researcher also maintained vigilance to mitigate any potential selection bias introduced by gatekeeper involvement, thereby upholding the methodological rigor and ethical standards central to qualitative inquiry. Research Instrument The primary research instrument was the researcher, who facilitated data collection, interpretation, and analysis of the participants lived experiences. To ensure comprehensive and unbiased data collection, interviews were conducted by a hired interviewer on behalf of the researcher, enabling authentic and rich insights into participants’ experiences. Within the phenomenological framework, the researcher served as the primary instrument for data analysis, applying reflexivity and sensitivity to accurately represent the essence of teachers’ lived experiences with AI utilization in English language teaching. This approach ensured methodological rigor while preserving data authenticity. Data collection was guided by an interview protocol designed according to Moustakas’ (1994) method, which fosters open, participant-driven dialogue to elicit rich, detailed narratives. The interview protocol was developed to elicit rich, detailed accounts of English teachers’ lived experiences with AI utilization in rural secondary schools, adhering to Moustakas’ (1994) transcendental phenomenological framework. It followed a three-part structure: initiating with rapport-building social conversation to foster a trusting environment, followed by informed consent administration addressing ethical considerations, and then engaging participants with open-ended questions. The core interview questions focused on teachers’ experiences in curriculum design and assessment using AI tools, challenges and limitations of AI utilization, and contextual factors influencing AI adoption. Supplementary prompts explored pedagogical challenges, student engagement, technological access, teacher readiness, ethics, and professional development. The conversational format allowed adaptive follow-up probes and encouraged depth through active listening and clarifications, ensuring authentic expression of participants’ perspectives (Shoozan & Mohamad, 2024). The protocol was iteratively refined through feedback and pilot testing to enhance rigor and contextual responsiveness. Moreover, data collection was supported by audio recording, transcription, and use of a research journal for memoing. Data analysis employed Moustakas’ (1994) processes of bracketing, phenomenological reduction, imaginative variation, and synthesis to reveal the essence of participants’ experiences. Data Gathering Procedure Data collection focused on in-depth conversational interviews with four English teachers from rural secondary schools in Central Philippines, aligned with phenomenological inquiry principles (Moustakas, 1994). Eligible participants were identified through gatekeepers, based on the study’s inclusion criteria. Prior to the interviews, participants received full disclosure about the study’s purpose, voluntary nature of participation, confidentiality assurances, and their right to withdraw at any time. Informed consent was then obtained from all participants. To ensure methodological rigor and reduce potential bias, a qualified interviewer was hired to conduct the sessions. His academic preparation, teaching experience, and training in research methods ensured that the interviews were carried out with competence, ethical sensitivity, and methodological soundness. The interviews were structured in three phases: rapport-building to establish trust and comfort, and the core discussion focused on open-ended questions exploring participants’ experiences with AI utilization, perceived challenges, opportunities, and contextual factors influencing AI utilization in their teaching practice. Throughout the interviews, the interviewer personally conducted all sessions, while the researcher used active listening techniques and follow-up prompts to deepen exploration while maintaining a natural conversational flow. The interview was closed by inviting the participant to share any final thoughts or insights that may not have been addressed earlier. The researcher thanked the participant sincerely and explained the next steps, including transcript review and the opportunity for the participant to confirm or clarify their statements. Sessions were audio-recorded with participant consent to ensure accurate capture of their narratives. After each interview, participants were given a modest token of appreciation as a gesture of thanks for their time and willingness to share their experiences. For the final phase, audio recordings were transcribed by the researcher and transcripts were subjected to member checking with participants to verify accuracy and enhance credibility of the data. For data analysis, Moustakas’ (1994) transcendental phenomenological method was employed. The process started with epoche (bracketing), where the researcher sets aside personal assumptions and biases to approach the data with openness and neutrality. This was followed by phenomenological reduction, in which the researcher identified significant statements or "meaning units" through repeated readings of each transcript. These units then underwent imaginative variation to explore the underlying structures and conditions that give rise to the participants' experiences. A textural description was developed to describe what the participants experienced, followed by a structural description that explained how these experiences unfolded within their specific contexts. The final step involved a synthesis of meanings and essences, integrating both the textural and structural elements to arrive at the essential meanings of the lived experiences of English Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9596 teachers regarding AI utilization. This rigorous approach ensured trustworthiness and provided rich, nuanced insights that could inform educational policy and practice regarding AI use in rural secondary schools (Ahmed, 2024; Shenton, 2004). Importantly, this method empowered participants to freely express their realities and construct personal narratives shaped by their experiences. The insights drawn help inform future policies, training, and support systems tailored for educators in similar settings (Ahmed, 2024; Shenton, 2004). Rigors of the Qualitative Research This study adhered to rigorous ethical standards and established criteria for trustworthiness in qualitative research to ensure the integrity of the research process and the protection of participants’ rights and dignity. Prior to data collection, ethical approval was obtained from the appropriate institutional review board or the relevant school administration. Participation in the study was strictly voluntary, and all participants were provided with comprehensive information regarding the study’s objectives, procedures, potential risks, and anticipated benefits. Informed consent was secured from all participants, and they were reminded of their right to withdraw from the study at any time without any negative consequences (Lincoln & Guba, 1985). Data Explication This study was analyzed using Moustakas’ (1994) transcendental phenomenological method to explore the lived experiences of the conversation partners. This approach allows the researcher to delve into the essence of the participants' experiences, providing rich insights into the phenomenon under investigation. In this study, data explication involved a comprehensive and detailed interpretation of the lived experiences of English teachers in rural secondary schools regarding the utilization of artificial intelligence (AI) in their instructional practices. The first phase of data analysis involved bracketing, wherein the researcher consciously sets aside personal biases, prior knowledge, and assumptions regarding artificial intelligence (AI) utilization. This process ensured that the analysis is approached with neutrality and openness to the participants’ lived experiences. The researcher began by immersing in the collected data, which included repeatedly listening to and transcribing audio-recorded interviews with English teachers from the rural secondary schools in the Central Philippines. Through this process, the researcher became deeply familiar with the nuances of the teachers’ perceptions and experiences related to AI utilization. The next step, the researcher engaged in phenomenological reduction by systematically identifying and extracting meaningful segments from the transcripts that represented the essence of the participants’ lived experiences. This phase focused on highlighting significant statements that captured the core of the teachers’ perceptions of AI utilization, such as the ways AI has impacted their instructional practices and their professional growth. The researcher paid particular attention to both positive and negative experiences, including opportunities for instructional innovation, challenges related to technological limitations, and the alignment or misalignment between AI-supported teaching and existing performance evaluation criteria. Followed by, phenomenological reduction, the researcher proceeded to imaginative variation, exploring multiple perspectives and possible meanings within the data. This step involved identifying invariant meaning units-the essential components of the participants’ experiences with AI utilization. These meaning units were then clustered into broader thematic categories, each representing a significant aspect of the phenomenon under study. The researcher ensured that these themes emerge organically from the data, maintaining fidelity to the participants’ voices and experiences. Next, is the Thematic Clustering and Interpretation. At this stage, the researcher undertook an in-depth interpretation of the identified themes, using imaginative variation to explore how these themes are interrelated and how they collectively shape the teachers’ experiences. The themes were clearly defined to reflect their essence, providing a comprehensive understanding of how English teachers perceive and navigate AI utilization in relation to their professional responsibilities. The researcher refined these definitions through a careful review of the data and ongoing reflective engagement, ensuring that the themes accurately represented the lived realities of the participants. Finally, the researcher synthesized the themes into a cohesive narrative that encapsulates the essence of the lived experiences of English teachers in the context of AI utilization in rural secondary schools. This synthesis integrated direct quotations from participants to provide authenticity and depth to the findings. The study thus contributed to a deeper understanding of the opportunities and challenges faced by English teachers in the AI era, particularly within the unique context of rural secondary schools in the Central Philippines. Ethical Considerations This study adhered to the highest ethical standards to ensure the dignity, rights, and welfare of all participants are protected throughout the research process. Before data collection, ethical clearance was obtained from the appropriate institutional review board or ethics committee to guarantee compliance with established ethical guidelines. Voluntary Participation and Informed Consent. Participation in this study was entirely voluntary. Informed consent was secured after a clear explanation of the study’s objectives, procedures, potential risks and benefits, and the participants’ right to withdraw at any time without penalty. The consent process also clarified how the data be recorded, stored, and used to maintain transparency Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9597 and respect for participant autonomy. PHREB (2022) mandates that consent must be documented and that confidentiality and privacy are rigorously protected throughout the research process. Confidentiality and Privacy. Strict confidentiality measures was implemented to protect all collected data. PHREB emphasizes that maintaining confidentiality and privacy fosters trust and protects participants from potential risks associated with participation in research (PHREB, 2017, 2022). Only authorized personnel have an access to identifiable information, and data were securely stored in both physical and digital formats. Participants was given an assurance that their information will not be disclosed outside the research context without their explicit permission. All personal identifiers were removed to protect participant privacy. Pseudonyms were used in transcripts and reports, and audio recordings will be stored in password-protected files and deleted after transcription and analysis. Transparency. The study committed to transparent research practices by clearly informing participants of the purpose, procedures, and intended use of data during the consent process. Data management and privacy measures were discussed in detail. Furthermore, participants were given the opportunity to review the summary of the findings to verify the accuracy of their represented views. Integrity. The research process reflected integrity through honest data collection and accurate reporting. As outlined by BERA (2018), any form of data fabrication, falsification, or misrepresentation undermines the credibility of research and will be strictly avoided. Conflicts of interest, if any, will be disclosed transparently in adherence to ethical guidelines. Justice. The principle of justice guided the equitable treatment of all participants, ensuring fair selection and distribution of the benefits and burdens of the research. This study was committed to inclusivity by involving teachers from farm schools, a group often marginalized or overlooked in educational research. By prioritizing their voices, the study sought to contribute to a fairer representation and resource allocation for rural educators, addressing systemic inequities in educational policy and support. Ensuring justice reinforces the study’s relevance by promoting social equity and empowering a community whose experiences are vital to understanding and improving rural education. Social Value. Social value refers to the relevance and potential impact of the research in addressing an existing social or health problem. The research must contribute to a better understanding of the issue and promote the well-being of individuals, families, and communities. The study prioritized social value by focusing on the lived experiences of English teachers navigating AI utilization. It highlighted the significance of addressing emerging educational technologies to ensure research findings are relevant and meaningful to contemporary teaching contexts. Vulnerability of Participants. Participants may face vulnerabilities related to geographic isolation, limited resources, and professional pressures. These contextual factors necessitate ethical sensitivity and protective measures to avoid coercion, harm, or exploitation. Researcher remained conscious of power dynamics and ensure participants are fully informed of their rights, including the option to skip any question or exit the study without consequence. Potential Risks and Benefits. Potential risks, such as emotional discomfort or breaches of confidentiality, were carefully considered and minimized through ethical safeguards like informed consent and secure data handling. These precautions helped balance the risks against the study’s anticipated benefits, such as deeper insights into AI’s impact on teaching and professional performance. Researcher Qualifications. The researcher possessed the appropriate qualifications and contextual understanding to conduct the study ethically. Cultural competence, combined with reflexivity and continuous self-assessment, ensures sensitivity to power imbalances and cultural nuances. This approach supports respectful engagement and accurate representation of participants’ lived experiences. Adequacy of Facilities. Data collection took place in facilities that are safe, accessible, and contextually appropriate. In rural environments where infrastructure may be limited, the researcher adapted research methods to minimize disruption to participants’ routines. This strategy reflects an ethically sound and context-sensitive research approach. Community Involvement. Engagement with the local community is integral to the study’s ethical framework. It involved local stakeholders throughout the research process from planning to dissemination, enhancing the study’s relevance, fostering shared ownership of findings, and promoting sustainable benefits for the educational community. RESULTS AND DISCUSSION This study explored the lived experiences of English teachers in rural secondary schools as they engaged with artificial intelligence (AI) tools in curriculum design, assessment practices, and classroom instruction. Guided by Moustakas’ (1994) transcendental phenomenological approach, the study sought to understand the contextual factors influencing teachers’ use of AI, the challenges they face, and the opportunities they perceive in applying AI within rural educational settings. The findings were organized into major themes and subthemes corresponding to these core research questions. In terms of contextual factors, teachers demonstrated the theme Enhancement of Pedagogical Competence by utilizing AI to support customized lesson planning and curriculum simplification. Through AI, they were able to unpack broad competencies into specific and actionable teaching strategies that aligned with learners’ readiness levels. Moreover, the theme Provision of Personalized Learning Pathways revealed teachers’ adaptability in aligning AI use with the realities of rural education. They modified AI- Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9598 generated materials to fit the learners’ socio-cultural and environmental contexts while contending with infrastructural barriers such as limited internet access, insufficient technological devices, and the persistent digital divide. With regard to challenges, two primary themes emerged: Limitations of AI Utilization and Adherence to Ethical Standards. Teachers encountered various technical limitations, including AI inaccuracies that required prompt refinement and filtering. These were compounded by the scarcity of technological resources and unstable connectivity that hindered seamless implementation. In terms of ethical challenges, teachers assumed a dual role enforcing academic integrity among students who tended to over-rely on AI and moderating responsible use by promoting balanced and mindful engagement with AI-generated content. As for opportunities, the findings highlighted two major themes: Elevating Assessments and Enhancing Engagement. Teachers maximized AI tools to develop tailored quizzes, rubrics, and test items, which streamlined assessment preparation and improved the accuracy and quality of written outputs. They also employed AI to enhance grammar checking and writing proficiency. Furthermore, AI contributed to increased teacher efficiency and reduced workload, allowing more focus on instruction and student engagement. The use of interactive and contextualized AI-generated materials fostered learner motivation and participation, demonstrating the potential of AI to enrich classroom experiences even in resource-constrained environments. Overall, the findings indicate that rural English teachers’ engagement with AI involves a continuous process of negotiation between innovation and constraint. While AI presents meaningful pedagogical benefits, its successful integration in rural schools depends on teachers’ contextual adaptability, ethical leadership, and capacity to manage infrastructural limitations. Thus, AI serves not as a replacement for human instruction but as a supportive tool that, when effectively contextualized, can enhance both teaching and learning in rural educational settings. A Simulacrum of Rural Secondary English Teachers' Journeys with AI: Contextual Factors, Challenges, and Opportunities CONCLUSION The findings affirm that while AI has the potential to enhance teacher efficiency, reduce workload, diversify assessments, and foster learner engagement, its educational value remains contingent upon critical mediation and contextualization by teachers. AI outputs, although useful as starting points, are often generic and require substantial adaptation to ensure relevance, comprehensibility, and Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9599 alignment with the learners’ socio-cultural environment. Teachers’ active efforts to localize and simplify AI materials by embedding local culture and community practices demonstrate that contextualization is central to sustaining meaningful instruction in rural settings. The study also underscores the necessity of ethical guidance in the adoption of AI. Teachers assumed dual roles as monitors of academic honesty and mentors of responsible technology use, ensuring that learners engage with AI in ways that support authentic skill development rather than dependency. This reflects the critical role of teachers as moral and pedagogical gatekeepers in an era where digital tools pose both opportunities and risks. At the same time, persistent challenges such as poor internet connectivity, limited access to digital devices, and the digital divide between teachers and students highlight that infrastructure remains a prerequisite for equity in AI utilization. Despite these systemic barriers, teachers demonstrated remarkable resilience and adaptability. By blending AI tools with traditional strategies such as printed modules, remedial tutorials, and peer-supported learning, they sustained instructional continuity and nurtured learner growth even under resource constraints. This resilience illustrates that meaningful utilization of AI in rural schools is less a function of technology itself and more a product of teachers’ creativity, adaptability, and professional commitment. Hence, the study affirms that equitable and effective English education in the AI era is possible when instruction is teacher-led, ethically grounded, and context-sensitive. Teachers’ capacity to critically mediate AI outputs, contextualize learning, and innovate within systemic limitations positions them as indispensable agents of transformation in rural education. By framing AI as a complement to, rather than a substitute for, professional expertise, rural educators sustain relevant, empowering, and studentcentered English instruction that bridges global technological tools with local realities. Finally, the study carries broader implications. For practice, it emphasizes the need for continuous professional development in AI utilization, contextualized pedagogy, and ethical moderation. For policy, it calls for systemic investment in digital infrastructure and equitable resource allocation for rural schools. For future research, it recommends broader and more inclusive inquiries that incorporate the perspectives of students, administrators, and communities, as well as comparative studies across diverse rural contexts. By situating rural teachers’ voices at the center, this study contributes to the discourse on AI in education, highlighting resilience, adaptability, and innovation as defining qualities of teaching in the digital era. REFERENCES 1) Alam, A. (2022). Challenges and barriers of integrating artificial intelligence in education in developing countries. Education and Information Technologies, 27(5), 6637–6655. https://doi.org/10.1007/s10639-022-11014-7 2) Aljanabi, M. (2023). Artificial intelligence in education: Opportunities, challenges, and ethical considerations. Education and Information Technologies, 28(5), 5897–5915. https://doi.org/10.1007/s10639-023-11508-6 3) Alkadi, A., & Ali, J. K. M. (2024). A holistic approach to ChatGPT, Gemini, and Copilot in English learning and teaching. Language Teaching Research Quarterly, 43, 155-166. https://doi.org/10.32038/ltrq.2024.43.09 4) Almalki, A., & Aziz, M. A. (2022). Teachers’ perceptions and readiness for AI integration in education: A systematic review. Journal of Educational Technology & Society, 25(1), 45-58. 5) APH Quality Handbook. (n.d.). Data collection - Qualitative research. Retrieved from https://aph-qualityhandbook.org/setup-conduct/methods-data-collection/2-1-qualitative-research/data-collection/ 6) Arnold, D., Thorne, S., & Skiba, D. (2021). Bridging the digital divide in rural education: Infrastructure, access, and equity challenges. Journal of Educational Technology Development, 14(2), 45–62. https://doi.org/10.1177/XXXX 7) Arnold, M. L., Sun, Y., & Chen, Y. (2021). Rural Education Challenges and Technology Integration: Bridging the Gap through Teacher Agency. Journal of Rural Studies, 82, 150–160. 8) Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education. Cengage Learning. 9) Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215 10) Biesta, G. (2015). Good education in an age of measurement: Ethics, politics, democracy. Routledge. 11) British Educational Research Association. (2018). Ethical guidelines for educational research (4thed.). https://www.bera.ac.uk/publication/ethical-guidelines-for-educational-research-2018 12) Burnett, C., & Merchant, G. (2020). Literacy as a social practice in the digital era: Implications for rural education. British Journal of Educational Technology, 51(5), 1628–1642. https://doi.org/10.1111/bjet.12918 13) Chamberlin, R. (2020). Conversational interviewing foundational techniques. 14) Chen, L., & Xie, H. (2022). AI-powered personalized learning in language education: Opportunities and challenges. Computers & Education, 182, 104495. 15) Consoli, S., Mariani, M., & Romano, A. (2023). Teachers’ digital competence and AI integration in curriculum design. Computers & Education, 194, 104672. https://doi.org/10.1016/j.compedu.2023.104672 16) Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage Publications. Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9600 17) Daud, A., Aulia, A. F., Muryanti, Harfal, Z., Nabilla, O., & Ali, H. S. (2025). Integrating artificial intelligence into English language teaching: A systematic review. European Journal of Educational Research, 14(2), 677-691. European Journal of Educational Research 18) Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.. 19) Dos Santos, M., Kim, H., & Yan, L. (2023). Using ChatGPT to scaffold L2 writing instruction in English language teaching. Journal of Language Learning, 12(4), 245-261. 20) Dwivedi, Y. K., Hughes, L., Baabdullah, A., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 21) Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1 22) Fullan, M. (2007). The New Meaning of Educational Change (4th ed.). Teachers College Press. 23) Garcia, M., & Medina, P. (2025). Teachers’ perceptions and challenges in integrating AI in rural Philippine schools. International Journal of Educational Research, 113, 102114. https://doi.org/10.1016/j.ijer.2024.102114 24) García-Peñalvo, F. J. (2021). Digital transformation in education: The use of AI and data analytics in teaching and learning. Future Internet, 13(8), 210. https://doi.org/10.3390/fi13080210 25) Heffernan, N., & Heffernan, C. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. https://doi.org/10.1007/s40593-014-0024-x. 26) Holmes, W., Bialik, M., & Fadel, C. (2021). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. 27) Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., & Koedinger, K. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 31(4), 1–23. https://doi.org/10.1007/s40593-021-00239-0 28) Howley, A., Howley, M., & Yahn, J. (2014). Rural school teachers and the digital divide: Reflecting on authentic integration. Peabody Journal of Education, 89(2), 200–215. https://doi.org/10.1080/0161956X.2014.897428 29) Huang, R., Spector, J. M., & Yang, J. (2019). Educational technology: A primer for the 21st century. Springer. https://doi.org/10.1007/978-981-13-6643-7 30) Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics for study success: Reflections on current empirical findings. Research and Practice in Technology Enhanced Learning, 15(4), 1–17. https://doi.org/10.1186/s41039-02000129-8 31) Imperial College London. (2010). Interview protocol design. Retrieved from Imperial College London. 32) Karki, B., & Karki, A. (2025). AI in English language teaching: Opportunities and limitations in rural contexts. Language Teaching and Learning Journal, 29(1), 45-59. 33) Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274 34) Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70. 35) Kukulska-Hulme, A. (2020). Mobile-assisted language learning [Revised and updated]. Language Learning & Technology, 24(1), 1-17. 36) Leu, D. J., Forzani, E., & Rhoads, C. (2023). Digital Literacy in Rural Education: Challenges and Opportunities. Reading Research Quarterly, 58(1), 47–66. 37) Li, X., & Ni, Y. (2021). Artificial intelligence in curriculum design: Opportunities and challenges for teachers. Journal of Curriculum Studies, 53(6), 791–809. https://doi.org/10.1080/00220272.2021.1882312 38) Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. 39) Moustakas, C. (1994). Phenomenological research methods. Sage Publications. https://doi.org/10.4135/9781412995658 40) Nieuwenhuis, J. (2016). Qualitative research designs and data-gathering techniques. In K. Maree (Ed.), First steps in research (pp. 70–97). Van Schaik Publishers. 41) OECD. (2021). AI in education: The role of policy for innovation and equity. Organisation for Economic Co-operation and Development Publishing. https://doi.org/10.1787/75e5c5b3-en 42) OECD. (2023). Artificial intelligence in education: Addressing policy challenges. OECD Publishing. https://doi.org/10.1787/XXXX Rural Secondary English Teachers’ Journeys With AI: Contextual Factors, Challenges, and Opportunities IJSSHR, Volume 08 Issue 12 December 2025 www.ijsshr.in Page 9601 43) Pham, H. H., & Ho, T. T. H. (2020). Toward a ‘new normal’ with e-learning in Vietnamese higher education during the COVID-19 pandemic: Bottom-up responses from the grassroots. Journal of Learning for Development, 7(3), 383–396. 44) Pham, T. H. (2021). Adapting English teaching strategies in rural schools: Addressing resource and learner limitations. Asian Journal of Education Research, 9(2), 56–68. 45) Piaget, J. (1954). The Construction of Reality in the Child. Basic Books. 46) Piety, P. J., & Hickey, D. T. (2018). Teachers and Technology: Supporting Teacher Agency and Improvisation in the Classroom. Curriculum Inquiry, 48(5), 527–543. 47) Pratschke, J. (2024). Generative AI as a natural fit for language education: Interactions and innovation. Journal of Computer-Assisted Language Learning, 37(1), 30-47.publication. https://doi.org/10.1080/00131911.2023.2177260 48) Redecker, C., & Johannessen, Ø. (2013). Changing assessment: Towards a new assessment paradigm using ICT. European Journal of Education, 48(1), 79–96. https://doi.org/10.1111/ejed.12018 49) Schleicher, A. (2019). OECD future of education and skills 2030: OECD learning compass 2030. OECD. 50) Shan, H., & Abdul Aziz, A. (2022). Creative pedagogical strategies in resource-limited rural schools: A case study. Journal of Rural Education Studies, 37(1), 22–38. 51) Sharma, R., & Singh, G. (2023). AI-driven pedagogical practices: Challenges and prospects in developing countries. Education and Information Technologies, 28(2), 1457–1475. https://doi.org/10.1007/s10639-022-11252-9 52) Tamim, R. M., & Borokhovski, E. (2022). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational Research, 92(2), 186–228. https://doi.org/10.3102/0034654321101915 53) Trucano, M. (2013). ICT in education in developing countries: Research, policy and practice. World Bank. 54) UNESCO. (2022). Reimagining our futures together: A new social contract for education. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000379707 55) UpMetrics Blog. (2022, July 25). 8 Essential qualitative data collection methods. Retrieved from https://blog.upmetrics.com/essential-qualitative-data-collection-methods 56) van Es, E. A., Conklin, H. G., Bieda, K. N., & Sherin, M. G. (2022). Adaptation of Digital Tools in Teaching: The Role of Teacher Judgment and Agency. Teaching and Teacher Education, 109, 103552. 57) Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press 58) Wang, Y., & Zhang, L. (2023). AI as a catalyst for teacher productivity and student engagement: A review of empirical evidence. Computers & Education, 194, 104704. https://doi.org/10.1016/j.compedu.2022.104704 59) Warner, K. (2023). Customizing content with ChatGPT: Engagement and localization in language teaching. TESOL Quarterly, 57(3), 710-729. 60) Warschauer, M., & Matuchniak, T. (2010). New technology and digital worlds: Analyzing evidence of equity in access, use, and outcomes. Review of Research in Education, 34(1), 179–225. https://doi.org/10.3102/0091732X09349791 61) Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. 62) Zhao, Y. (2021). Build back better: How (and why) we must transform education post-pandemic. Journal of Educational Change, 22(4), 509–529. https://doi.org/10.1007/s10833-021-09417-3 There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.