Teaching Reinforcement Learning in Large-Scale Interdisciplinary Classrooms: A Course Design and Practice

Wang Feiyue

Abstract

Reinforcement learning (RL) has become a foundational paradigm in modern artificial intelligence, yet its technical complexity poses significant challenges for large-scale interdisciplinary education. Students from diverse academic backgrounds often face difficulties in understanding abstract concepts, maintaining engagement, and applying RL methods in their own domains. This paper presents the design and implementation of a reinforcement learning course tailored for large-scale interdisciplinary classrooms. The course integrates structured content progression, real-world analogies, and interactive learning activities to bridge disciplinary gaps and improve learning accessibility. Key design elements include cognitive scaffolding through everyday scenarios, level-based task progression from fundamental concepts to advanced algorithms, and an interactive feedback system based on QR-code quizzes that promotes continuous participation. The course was implemented in a large cohort of students from multiple disciplines, and preliminary results indicate improved engagement, participation consistency, and conceptual understanding. The findings suggest that carefully designed instructional strategies can effectively support reinforcement learning education in large, diverse classroom settings. This work provides practical insights and a scalable approach for teaching complex AI topics in interdisciplinary contexts.

Artificial Intelligence for Education

Teaching Reinforcement Learning in Large-Scale Interdisciplinary Classrooms: A Course Design and Practice

Abstract

Citation

Share