Kovari A. A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade. Soc Sci Humanit Open. 2025;11:101335. doi:10.1016/j.ssaho.2025.101335.
García Dieguez M. Comment on the AI-Powered Collaboration: When Artificial Intelligence Enhances Peer Learning. [Internet]. Pan American Health Organization. Bibliographic Repository. Cited on 07/10/2025. Available at: https://campus.paho.org/en/repo/powered-collaboration-when-artificial-intelligence-enhances-peer-learning
García Dieguez M.
CEEProS Universidad Nacional del Sur
This article is highly relevant for educators, instructional designers, and leaders of online programs interested in understanding how artificial intelligence can strengthen collaborative learning in higher education. The review synthesizes studies published over the last decade on technologies such as predictive learning analytics, recommender systems, chatbots, intelligent tutoring systems, automated feedback, immersive environments, and emotional or physiological monitoring. Although the review is not specifically focused on health professions education, its findings are highly transferable to the continuing professional development of healthcare professionals, particularly in case-based learning, simulation, peer learning, communities of practice, and collaborative feedback.
The review included 27 studies selected according to PRISMA criteria. It demonstrates a marked increase in publications on AI-supported collaborative learning, particularly between 2023 and 2024. The findings suggest that artificial intelligence can enhance collaborative learning by personalizing learning pathways, predicting learner performance, identifying at-risk students early, providing immediate feedback, supporting group work, and monitoring participation.
The article highlights that AI systems can assist in group formation, adjust task difficulty, monitor individual contributions, detect low participation, and provide recommendations to improve collaboration. It also emphasizes the importance of social presence and emotional engagement, noting that some AI-based tools can analyze facial expressions, voice tone, or interaction patterns to identify difficulties in group dynamics. In addition, the review highlights the educational potential of virtual laboratories, the metaverse, simulations, chatbots, and feedback-feedforward systems to promote more interactive learning experiences.
An important limitation is the restricted search strategy, which included only English-language publications. Furthermore, many of the included studies originated from engineering, general education, or educational technology rather than health professions education, requiring careful contextual adaptation before implementation in medical education or continuing professional development. The review also raises important ethical concerns, including algorithmic transparency, data protection, bias, technological dependence, and the need to maintain human oversight.
- Use AI to personalize collaborative learning activities.
- Apply learning analytics to identify at-risk participants, low interaction, or unequal contributions within teams.
- Design adaptive collaborative tasks in which AI can provide additional support when groups struggle or increase task complexity as performance improves.
- Incorporate automated feedback and feedforward tools.
- Use chatbots or virtual assistants to support group work, answer frequently asked questions, provide methodological guidance, and send task reminders.
- Maintain active faculty supervision throughout the learning process.
- Establish clear ethical guidelines for the use of AI.