A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents

This paper presents Inquizzitor, an LLM-based formative assessment agent for middle school STEM+C education that provides adaptive scaffolding through theory-driven dialogue. The framework integrates Evidence-Centered Design with Social Cognitive Theory to deliver pedagogically sound feedback and assessment grounded in cognitive science principles.

Large language models (LLMs) present new opportunities for creating pedagogical agents that engage in meaningful dialogue to support student learning. However, the current use of LLM systems like ChatGPT in classrooms often lacks the solid theoretical foundation found in earlier intelligent tutoring systems. To bridge this gap, we propose a framework that combines Evidence-Centered Design with Social Cognitive Theory for adaptive scaffolding in LLM-based agents focused on STEM+C learning. We ill

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