AI RESEARCH
A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents
arXiv CS.CL
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ArXi:2508.01503v2 Announce Type: replace Large language models (LLMs) present new opportunities for creating pedagogical agents that engage in meaningful dialogue to student learning. However, current LLM systems used in classrooms often lack the solid theoretical foundations found in earlier intelligent tutoring systems. To bridge this gap, we propose a framework that combines Evidence-Centered Design with Social Cognitive Theory and Zone of Proximal Development for adaptive scaffolding in LLM-based agents focused on STEM+C learning.