This article, titled “Socially shared regulation of learning and artificial intelligence: Opportunities to support socially shared regulation,” explores the potential of Artificial Intelligence (AI) technologies to address challenges in supporting high-level socially shared regulation of learning (SSRL) in online collaborative learning (OCL) contexts. Despite the promise of AI, the effective application of AI to support the multifaceted areas (cognition, metacognition, and motivation) and phases (forethought, performance, and reflection) of SSRL remains underexplored. Furthermore, there is limited research on the pedagogical attributes and elements required for AI to effectively support students’ SSRL.
To bridge these gaps, this study aimed to investigate students’ perceptions of AI applications in enhancing SSRL and to identify the essential pedagogical elements necessary for AI to support SSRL during OCL. The research employed Focus Group Interviews with 30 undergraduate and graduate students, utilizing 9 scenarios of AI application storyboards and paper prototypes to facilitate in-depth discussions.
The findings reveal that students perceive various types of AI as beneficial for supporting cognitive, metacognitive, and motivational areas across different SSRL phases. Notably, students viewed AI not merely as a technological tool but as an active learning agent, capable of fulfilling roles previously exclusive to human educators and students. The study also identifies seven key pedagogical elements across TPACK (Technological Pedagogical Content Knowledge) components that students consider crucial for AI to effectively support SSRL in OCL environments. These insights offer significant implications for the design and utilization of educationally relevant AI to enhance SSRL in online collaborative settings.
Reference:
Kim, J., Detrick, R., Yu, S., Song, Y., Bol, L., & Li, N. (2025). Socially shared regulation of learning and artificial intelligence: Opportunities to support socially shared regulation. Education and Information Technologies, 30, 11483–11521. https://doi.org/10.1007/s10639-024-13187-9
