Integrating Game-Based Learning and Generative AI in Programming Education: A Study on Automated Question Generation and Learning Outcomes Enhancement

Abstract

This study examined the instructional effects of integrating a game-based learning system into a programming course, focusing on how tool-supported practice influences students’ learning outcomes and learning experiences. A quasi-experimental design was employed, involving an experimental group that used a game-based learning system for programming practice and a control group that completed traditional programming assignments. Both groups were taught by the same instructor using identical instructional content over an eight-week period. Pre-tests and post-tests were administered to assess learning performance. Baseline-adjusted and conditional effect analyses were conducted to examine whether the instructional effect varied according to students’ prior programming knowledge. The results showed that students in the experimental group achieved higher post-test performance than those in the control group, and the Group × Pre-test interaction indicated that the learning effect was conditional on learners’ baseline programming competence. In addition, students in the experimental group completed questionnaires on system use perceptions and flow experience. The findings indicated generally positive perceptions of the game-based learning system and a significantly positive level of flow during programming practice. The findings suggest that the GBPLS can support programming practice when it is embedded within a coherent instructional design. However, the observed benefits should be interpreted as conditional rather than universal. The educational value of the system appears to depend on the alignment among programming tasks, feedback, game-based engagement, generative AI-supported question generation, and teacher guidance.