Scalable SRL Conversational Scaffolding for Student–LLM Interaction

Abstract

The growing adoption of Large Language Models (LLMs) is transforming learning in higher education. While they provide flexible support, concerns remain about over-reliance, uncritical acceptance of outputs, and excessive cognitive offloading. To address these challenges, we foreground self-regulated learning for LLMs (SRL-for-LLM), defined as learners' ability to plan, monitor, and reflect on AI interactions to support learning. Guided by SRL theory, we introduce ChatWise, a proof-of-concept browser extension that combines conversational scaffolding with learning analytics to classify student prompts by SRL strategies and deliver adaptive metacognitive feedback in real time. A two-phase study included the design of ChatWise and a within-subject field study with STEM undergraduates. Log analyses using mixed-effects modeling showed that access to ChatWise more than doubled the likelihood of producing high-quality prompts. Findings further indicate improved prompt refinement, greater strategic awareness, and more reflective engagement with AI-supported learning. These results highlight the potential of SRL-aligned, analytics-driven scaffolding to support more effective student–LLM interactions.