Exploratory study of an AI-supported discussion representational tool for online collaborative learning in a Chinese university

With the aid of artificial intelligence (AI), it is more feasible to leverage discussion data to understand the online collaborative learning process. This paper presented an AI-supported discussion representational tool (integrating behavioral and cognitive representations) aimed at enhancing online collaborative learning from three aspects: motivation, cognitive presence, and learning performance. A randomized controlled trial (RCT) was conducted to examine the tool's effectiveness with 122 students in four groups: (1) behavioral representation (n = 31), (2) cognitive representation (n = 31), (3) mixed mode (combining behavioral and cognitive representations, n = 30), and (4) a control group (n = 30). Results indicated that: (1) the discussion representational tool did not significantly enhance students' motivation but led to significant gains in their learning performance compared to the control group; (2) students who learned with the discussion representational tool showed significant improvements in higher-order cognitive presence, ordered network analysis revealed that they generated more higher-level cognitive connections; (3) the motivation is an effective predictor of cognitive presence and learning performance, and discussion representational tool positively moderated the relationship between motivation, cognitive presence, and learning performance. These findings represent a new contribution of the AI-supported discussion representational tool to facilitate online collaborative learning.