An Artificial Intelligence‐Enabled Group Cognitive Diagnosis Approach With the Goal of Promoting Online Collaborative Learning

ABSTRACT Background Online collaborative learning has been broadly applied in the field of higher education. Nevertheless, not all types of collaborative learning can produce the desired learning results. Objectives To facilitate online collaborative learning, the present study proposed an innovative artificial intelligence‐enabled group cognitive diagnosis approach with the goal of improving online collaborative learning. Methods A total of 135 college students was included in the current study and divided into 45 groups. A total of 15 groups consisting of 45 students used the group cognitive diagnosis approach. An additional 15 groups were assigned to the group knowledge graph approach, while the remaining 15 groups were assigned to the traditional online collaborative learning approach. Results and Conclusions The findings of this research indicated that the group cognitive diagnosis approach had more significant and positive impacts on collaborative learning performance, knowledge elaboration, and higher‐order cognitive engagement than did the group knowledge graph and traditional online collaborative learning approaches. Implications The current study deepens our understanding of group cognition and the corresponding complex interactions and provides a new method for improving online collaborative learning.