Teaching Literature in the Face of Artificial Intelligence in Education: Challenges and Coping Strategies of English Teachers
DOI:
https://doi.org/10.5281/zenodo.19890103Keywords:
Artificial Intelligence, , Literature Teaching, , Pedagogical Challenges, Coping Strategies, AI in EducationAbstract
This study explored the experiences of English teachers in teaching literature in the context of artificial intelligence (AI) in education, with particular focus on the challenges they encountered, the coping strategies they employed, and how they navigated AI integration while preserving traditional pedagogical practices. Anchored in Reader-Response Theory, Technological Pedagogical Content Knowledge (TPACK), and Technostress Theory, the study utilized a descriptive qualitative research design. Fifteen higher education English instructors from selected institutions in Zamboanga City were purposively selected as participants. Data were collected through a validated semi-structured interview guide and analyzed using thematic analysis. The findings revealed several interconnected challenges, including diminished student engagement in deep literary interpretation, overreliance on AI-generated outputs, difficulties in ensuring academic integrity, limited teacher preparedness in AI use, and challenges in adapting instructional strategies within evolving technological environments. In response, teachers employed various coping strategies such as reinforcing critical thinking through guided discussion, modifying assessment practices to ensure authenticity, establishing clear guidelines on AI use, integrating AI as a supplementary tool, and engaging in self-directed learning to improve AI literacy. Teachers also navigated AI integration by balancing technological tools with discussion-based instruction, emphasizing human interpretation, and redesigning learning activities that promote critical engagement. The study concludes that AI significantly reshapes literature teaching, requiring teachers to continuously adapt while preserving the interpretive and humanistic nature of the subject. These findings provide valuable insights for improving instructional practices, strengthening teacher training, and informing institutional policies in AI-mediated learning environments.
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