# A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents

arXiv: https://arxiv.org/abs/2508.01503 · HTML: https://mlsanity.com/paper/2508.01503/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach](https://mlsanity.com/paper/2407.16833/) (2024)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences](https://mlsanity.com/paper/2405.04645/) (2024)
- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [Using Generative AI and Multi-Agents to Provide Automatic Feedback](https://mlsanity.com/paper/2411.07407/) (2024)
- [Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)](https://mlsanity.com/paper/2505.17238/) (2025)

## Built on by (most cited first, 7 of 7)

- [Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agents](https://mlsanity.com/paper/2602.01415/) (2026)
- [BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation](https://mlsanity.com/paper/2602.13280/) (2026)
- [Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments](https://mlsanity.com/paper/2606.15766/) (2026)
- [Socrates went Nuclear: Comparing Interaction Strategies for AI systems in a Learning Context using Brain Sensing](https://mlsanity.com/paper/2609.00584/) (2026)
- [From Content Generation to Learning Support: Pedagogy-Guided Generative Video Tutors for STEM Learning](https://mlsanity.com/paper/2609.24083/) (2026)
- [RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning](https://mlsanity.com/paper/2610.13399/) (2026)
- [Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop](https://mlsanity.com/paper/2610.15692/) (2026)

## Abstract (arXiv)

Large language models (LLMs) present new opportunities for creating pedagogical agents that engage in meaningful dialogue to support 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. We instantiate this framework with Inquizzitor, an LLM-based formative assessment agent that integrates human-AI hybrid intelligence and provides feedback grounded in cognitive science principles. Our findings show that Inquizzitor delivers high-quality assessment and interaction aligned with core learning theories, offering effective guidance that students value. This research demonstrates the potential for theory-driven LLM integration in education, highlighting the ability of these systems to provide adaptive and principled instruction.