Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance

• Published in: Journal of research on technology in education Vol. 57; no. 4; pp. 727 - 753
• Main Authors: Zheng, Lanqin, Fan, Yunchao, Gao, Lei, Huang, Zichen, Chen, Bodong, Long, Miaolang
• Format: Journal Article
• Language: English
• Published: Routledge 04.07.2025
• Subjects: group performance; knowledge building; knowledge graph; Online collaborative learning; personalized recommendation; socially shared regulated behavior
• ISSN: 1539-1523; 1945-0818
• DOI: 10.1080/15391523.2024.2304066

As an effective form of pedagogy, online collaborative learning has received increasing application in the field of education. However, learners often feel frustrated with regard to knowledge building, cognitive engagement, and socially shared regulation. To solve these problems, the current study proposed an approach featuring artificial intelligence (AI)-empowered assessments and personalized recommendations. The purpose of this quasi-experimental study was to examine the effect of the proposed approach on collaborative learning performance. In total, 135 college students, who were divided into three conditions, participated in the current study. The results of both quantitative and qualitative analysis indicated that the proposed approach could substantially enhance collaborative knowledge building, cognitive engagement, socially shared regulated behaviors, and group performance. The findings are discussed thoroughly alongside their pedagogical and technological implications.