# Tailoring Scaffolding to Diagnostic Strategies: Theory-Informed LLM-Based Agents

arXiv: https://arxiv.org/abs/2605.04996 · HTML: https://mlsanity.com/paper/2605.04996/

## Key facts

- Cited by 1 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 1 of its 3 references that are in the map.
- First posted on arXiv 6 May 2026.

## 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, 1 of 1)

- [Structuring versus Problematizing: How LLM-based Agents Scaffold Learning in Diagnostic Reasoning](https://mlsanity.com/paper/2604.09158/) (2026)

## Built on by (most cited first, 1 of 1)

- [RegulaTutor: A Theory-Informed Framework for Long-Term Personalized Learning](https://mlsanity.com/paper/2610.13399/) (2026)

## Abstract (arXiv)

Learning analytics systems increasingly integrate large language models (LLMs) to provide adaptive scaffolding in complex learning environments, yet personalization is often driven by global instructional choices rather than principled alignment with learning theory, limiting effectiveness and pedagogical grounding. In prior work, we examined how structuring and problematizing scaffolding approaches can be instantiated through LLM agents in a scenario-based learning environment for diagnostic reasoning. While both approaches supported learning, we observed systematic differences in learner interaction patterns and clear tendencies indicating that different diagnostic strategies benefited from distinct forms of scaffolding. Building on these findings, we propose a theory-informed scaffolding design grounded in the Knowledge Learning Instruction (KLI) framework, as different diagnostic strategies target different types of knowledge and require different instructional mechanisms. We use KLI to guide the alignment between strategy demands and scaffolding approaches and introduce a KLI-informed hybrid LLM agent that adapts its pedagogical support according to the diagnostic strategy being practiced, rather than applying a single global scaffolding approach. We hypothesize that this design could enable better learning gains.