A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade

Abstract

This review examines the current state of integration and impact of AI-enhanced collaborative learning in the higher education sector. Given the rapid advances in technology, AI has enormous potential for application in educational settings, with benefits in terms of personalizing learning, better engaging learners and improving learning outcomes. Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. Furthermore, this review concludes that predictive analytics and multimodal approaches supported by artificial intelligence have been shown to enhance student engagement and motivation, while personalized learning systems and recommender algorithms ensure the effectiveness of collaborative learning environments. It also identifies two other critical issues: good task design and effective emotional engagement and social presence in AI-based environments. In addition, it highlights some of the problems and ethical considerations arising from the integration of AI like transparency, data protection, and a balance between full automation and human touch. This review aims to integrate the current state and future opportunities of AI-enhanced collaborative learning within a higher education context to inform educators, researchers, and policy makers in pursuit of improving teaching and learning practices.