GenAI–Supported Socially–Shared Regulation in Online Collaborative STEM Inquiry Learning. How students actively recruit AI as a regulatory partner using the GenAIRL tool.
Ucan, S., Webb, M., & Galamba, A. (2025). Recognising Need, Requesting Support: GenAI–Supported Socially–shared Regulation in Online Collaborative STEM Inquiry Learning. IFIP TC3 OCCE 2025.
Generative AI for Regulated Learning.
A Google Docs add-on tool designed on the CHARM Framework (Collaborative Human-GenAI Regulation Model). Unlike generic chatbots, GenAIRL is context-aware and designed specifically to scaffold regulatory skills.
Participants: 4 groups (N=14) of undergraduate bio-engineering students.
Task: 8-week collaborative inquiry project on marine pollution in the Marmara Sea.
Data: Mixed-methods analysis of 119 AI queries, time-stamped logs, and 16 Zoom session recordings.
GenAI often risks fostering over-reliance. This study investigates the specific "Trigger Events" that prompt students to voluntarily recruit AI as a partner, and the nature of their requests.
74.1% of student triggers were metacognitive (planning, monitoring, evaluating) rather than simple knowledge gaps. Students use GenAIRL to regulate their work, not just do it.
The most frequent request (43.3%) was for Regulatory Support, specifically seeking evaluation of work and ideas. GenAIRL acted as an auditor for group progress.
Highly predictable Trigger-Request patterns emerged. E.g., Internal monitoring ("Are we on track?") directly triggered requests for external AI validation.
Experience the Trigger-Request loop. Act as a student group encountering a challenge and see how GenAIRL responds to support socially shared regulation (SsRL).
Your group is stuck. What is the problem?
Observable event signals need
Group identifies regulatory gap
Formulate query to GenAIRL
Detect, Diagnose, Support
Interpret & Negotiate support