The Impact of AI-Based Collaborative Learning Methods on University Students' Learning Outcomes in Programming Courses

• In Event: The Role of Artificial Intelligence in Transforming Learning, Assessment, and Educational Practices

Abstract

This study examines the impact of AI-Based Collaborative Learning (AI-CL) on university students' programming course outcomes. By integrating Artificial Intelligence Generated Content (AIGC) into traditional Computer-Supported Collaborative Learning (CSCL), the research explores effects on cognitive (learning achievement, cognitive load) and non-cognitive (self-efficacy, learning interest) dimensions. A quasi-experimental design with 45 undergraduates revealed that the AI-CL group significantly outperformed the CSCL group in learning achievement, demonstrated lower cognitive load, and exhibited higher self-efficacy and learning interest. These findings underscore AI-CL's potential to enhance learning effectiveness, providing robust empirical support for broader AI integration in educational frameworks. The results suggest promising avenues for leveraging AI to create more effective and engaging learning environments in higher education.

Authors

• Haoming Wang, East China Normal University

• Zhan Chen, East China Normal University

• Jiaqi Wang, Hainan Normal University

• Xu Wang, East China Normal University

• Xianlong Xu, East China Normal University