Integrating Local Story and Generative AI in a Narrative Writing Microlearning Model: Exploring Students’ Digital Literacy And Character Development In Indonesian EFL Higher Education

This mixed-methods study examines the pedagogical integration of local story and Generative Artificial Intelligence (GenAI) within a structured six-phase narrative writing microlearning framework. Conducted with twenty-nine undergraduate English as a Foreign Language (EFL) students in East Java, Indonesia, the research evaluates the dual impact of this model on students' digital literacy and character development. Grounded in the Indonesian Ministry of Basic and Secondary Education's shift toward a Deep Learning approach (incorporating meaningful, mindful, and joyful learning) and the Outcome-Based Education (OBE) paradigm, the curriculum was designed to build measurable, career-ready competencies in L2 narrative composition, critical digital evaluation, and socio-ethical reflection. Quantitative data from student questionnaires and self-assessment checklists were triangulated with qualitative thematic analyses of observer logs, open-ended responses, and comparative examinations of student writing drafts. The quantitative analysis revealed high levels of student motivation, technical proficiency, and perceived relevance, validating the joyful and meaningful dimensions of the model. However, critical digital literacy items—specifically the capacity to reject irrelevant AI-generated feedback—registered lower scores, indicating a developmental gap in mindful learning that requires intentional, OBE-aligned academic scaffolding. Qualitative writing analysis demonstrated that this deep human-AI collaborative process enabled a transition from basic textual outputs to morally complex, localized cultural narratives. The paper concludes with a conceptual model for balanced human-AI pedagogical integration, addresses technological design limitations, and offers recommendations for contemporary language classrooms.