Abstract
This qualitative narrative inquiry, rooted in the interpretive paradigm, explores college instructors' lived experiences with generative AI tools in lesson planning and student assessment at Southern Masbate Roosevelt College, Philippines, during School Year 2025–2026. Guided by Clandinin and Connelly’s (2000) three-dimensional narrative space (temporality, sociality, place), data were collected from 10 purposively sampled instructors (Patton, 2015) using semi-structured interviews, reflective journals, and document artifacts (e.g., AI-generated lesson plans). Sample sufficiency was achieved through data saturation (Saunders et al., 2018), with methodological and data source triangulation enhancing credibility.
Key findings indicate AI drives adoption through time savings, efficiency, and customization (RQ1–2; Technology Acceptance Model, Davis, 1989; TPACK, Mishra & Koehler, 2006), streamlining planning/assessment/feedback (RQ3; Black & Wiliam, 1998). Challenges include inaccuracies, student over-reliance, and cheating (RQ4; UNESCO, 2023; OECD, 2024), mitigated by experienced instructors' ethical judgment (RQ5; Rogers, 1962). In rural Masbate, AI augments teaching but requires human oversight.
Recommendations: TPACK training, institutional AI policies, and mixed-methods studies for broader impacts. This underscores balanced AI integration for equitable education.
Keywords: Generative AI, narrative inquiry, lesson planning, student assessment, TPACK, rural education
DOI 10.65494/pinagpalapublishing.240
