A Study on the Development of an Education Model for Enhancing AID Competency through Case Analysis of Generative AI-Integrated Courses

Abstract

This study developed an AID (AI+Digital) Competency Enhancement Education Model by analyzing six generative AI-integrated courses at S University in 2025. Using Yin's multiple case study design, six courses across five disciplinary tracks were analyzed along four axes: teaching-learning, assessment, AI utilization, and competency design, drawing on course documents, student outputs, reflective journals, and surveys. Three AI utilization patterns were tentatively observed in these cases (Visualization, Hybrid Creative, and Simulation), alongside three shared patterns of change in teaching and learning: learners as active inquirers, critical verification as the core activity, and instructors as learning designers. The resulting AID Model integrates four design axes in a cyclical Explore→Generate→Verify→Reflect structure, with Human-in-the-Loop verification as a mandatory stage, a triple assessment structure (process, outcome, reflection), and five cumulatively developed competencies. Developed by iteratively comparing a theory-based analytic framework with real classroom data, the model addresses gaps in prior research including neglect of disciplinary specificity, optional treatment of verification, lack of integrated frameworks, and weak empirical grounding.