Can an AI assistant grounded in self-regulated learning theory help students plan, monitor, and reflect on their learning without doing that thinking for them?
Scaffolding Student Self-Regulated Learning with Large Language Models
Use LLMs and self-regulated learning (SRL) theory to build a full-stack web app that scaffolds students across forethought, performance, and self-reflection phases (e.g., goal-setting, planning, monitoring, reflection).
Objectives
- Design SRL-guided workflows (goal-setting, planning, monitoring, reflection) powered by LLMs
- Implement a privacy-aware prototype full-stack web app (e.g., Next.js/Node) that logs interactions for research with consent
- Evaluate usability and learning outcomes in pilot studies; iterate on prompts and UX
- Document prompts, safety policies, and release reproducible artifacts