Education and Information Technologies 2025
Impact of AI-agent-supported collaborative learning on the learning outcomes of University programming courses
Corresponding author: Xianlong Xu
DOI 10.1007/s10639-025-13487-8
First published online: 2025-03-13.Issue publication: 2025-08.
Research summary
This six-week study compares AI-agent-supported collaborative learning with computer-supported collaborative learning in a university programming context. The 45 participants were allocated to groups of 24 and 21. The AI-supported group achieved stronger learning outcomes, self-efficacy and interest, with lower mental effort; the difference in mental load was not significant.
Study design.
Six-week quasi-experimental comparison, with 45 undergraduates in an ACM programming competition teaching environment.
Abstract
With the rapid development of artificial intelligence technology in the field of education, AI-Agents have shown tremendous potential in collaborative learning. However, traditional Computer-Supported Collaborative Learning (CSCL) methods still have limitations in addressing the unique demands of programming education. This study proposes an innovative AI-Agent-supported Collaborative Learning (AI-CL) approach, aimed at enhancing students' programming learning experience and effectiveness through intelligent assistance from AI-Agents. The research developed an AI-Agent system based on large language models and employed a quasi-experimental design to investigate the effects of the AI-CL method on students' learning achievement, self-efficacy, cognitive load, and learning interest. The experiment was conducted at a university in Shanghai, recruiting 45 undergraduate students who were randomly assigned to the AI-CL experimental group (n = 24) and the CSCL control group (n = 21). The experimental context was set in an ACM programming competition teaching environment, lasting for 6 weeks with 70-min sessions each week. Results showed that the AI-CL group significantly outperformed the CSCL group in learning achievement, self-efficacy, and learning interest. Regarding cognitive load, the AI-CL group demonstrated significantly lower mental effort compared to the CSCL group, while there was no significant difference in mental load. These findings not only provide new theoretical perspectives for the application of cognitive load theory and self-efficacy theory in AI-assisted learning environments but also offer strong practical guidance for higher education institutions to introduce AI-Agent-assisted collaborative learning models in programming courses.
Research context
What evidence compares AI-agent-supported and conventional computer-supported collaborative programming?
Use this study when examining AI agents as collaborators in university programming education, especially achievement, self-efficacy and distinct dimensions of cognitive load.
AI agents · collaborative learning · programming education · cognitive load
中文研究介绍
研究在大学编程教学中开展六周比较，将45名学生分为24人的AI智能体协作学习组与21人的计算机支持协作学习组。AI组在学习成绩、自我效能与兴趣方面表现更好，心理努力较低，心理负荷的组间差异未达到显著水平。
Read and cite
Wang, H., Wang, C., Chen, Z., Liu, F., Bao, C., & Xu, X. (2025). Impact of AI-agent-supported collaborative learning on the learning outcomes of University programming courses. Education and Information Technologies, 30(12), 17717-17749. https://doi.org/10.1007/s10639-025-13487-8