Exploring the impact of AI technology on student collaborative learning based on a multi-layer network perspective

Collaborative learning (CL) hinges on promoting knowledge exchange through effective social interactions at the individual, group, and community levels, enabling knowledge to be efficiently constructed during the collaborative process: collaborative knowledge construction (CKC). However, with technological advancement, generative artificial intelligence and other intelligent learning tools have gradually become important facilitators of student learning. For CL, the traditional three-tier structure faces a strong impact from AI tools. To further explore the role of AI technology in CL and its impact on the quality of student CL, this study employs multi-layer network analysis (MNA) to model and quantify changes in the cognitive strategies of Chinese college students in CL, as impacted by AI technology. The impact of AI technology on the quality of student CL is verified through comparison with control group grades. The results show that (1) under the intervention of AI, the quality of students’ CL outcomes shows an upward trend, whereas the quality of the CL process significantly decreases. (2) AI technology occupies a dominant position in students’ cognitive and behavioural strategies in CL and is gradually becoming an independent hierarchical structure in CL.