AI-enabled blended teaching in Information Theory and Coding: an outcomes-based mixed-methods approach

With the rapid development of generative AI (GAI), it is a necessity to explore its effective integration into engineering education. This exploratory study incorporates GAI into an outcome-based education (OBE) framework for Information Theory and Coding (ITC) engineering education using mixed methods analysis of design logic, implementation paths, and learning mechanism. Quantitative analysis revealed that a “Planning–Verification” pattern where the AI outputs are actively cross-validated had significantly higher learning than passive reliance, resulting in a “productive friction” in terms of the emergence of low-performance students and advanced students. By adding verification checkpoints, AI-assisted activities aligned to intended learning goals, encouraging critical thinking and academic integrity. The study provides a transferable design framework and evidence-based guidance for STEM educators to implement AI in engineering education.