Flipped design thinking with generative AI for digital literacy in ODL

Abstract

Purpose – This study examines how three instructional conditions – Conventional Tutorial, Flipped Classroom Design Thinking (FCDT), and an AI-supported FCDT-AI model using ChatGPT – shape undergraduate students' Digital Literacy within an open and distance learning (ODL) environment at Universitas Terbuka, Indonesia. It responds to the growing need for scalable pedagogical models that integrate flipped learning, design thinking, and generative AI across Asian open universities. Design/methodology/approach – A within-subjects repeated-measures design was employed with 26 undergraduate students enrolled in an Academic Writing Techniques course. All participants experienced the three conditions in counterbalanced order via TUWEB, the institutional learning management system. Digital Literacy was measured after each condition using a multidimensional performance-based questionnaire. Quantitative analysis used Huynh–Feldt-adjusted repeated-measures ANOVA with Holm-adjusted post-hoc tests, while qualitative reflection logs were examined using reflexive thematic analysis to elucidate mechanisms underlying observed differences. Findings – A significant and substantial main effect of instructional condition was identified, demonstrating a clear performance gradient: Conventional < FCDT