Effects of Collaborative Generative Learning on Learners With Different Motivation Levels in AI-Enabled Immersive Virtual Environments

This study adopted a comparative experimental design to investigate the effects of collaborative generative learning (CGL) in an AI-enabled immersive virtual environment (AI-IVE) on learners with different motivation levels. Learners with high motivation levels (HML) showed strong interest in both the AI-IVE and the subject content, whereas learners with low motivation levels (LML) lacked interest in one or both aspects. A total of 67 ninth-grade students from two intact classes participated in the study. The classes were randomly assigned to either the experimental group or the control group. In the experimental group, students engaged collaboratively in generative learning activities using a structured CGL strategy with clearly defined roles, while in the control group, students completed the same activities individually using an individual generative learning (IGL) strategy. The results indicated that the CGL strategy enhanced both human-computer and learner-learner interactions, leading to improved learning outcomes in the AI-IVE. These effects were particularly evident in three aspects: (1) improved academic performance, knowledge retention, and transfer among LML learners; (2) increased situational interest, engagement, and self-efficacy; and (3) reduced cognitive load. These findings provide meaningful insights for the design and implementation of generative learning in AI-IVEs.