# Reasoning Trajectories for Socratic Debugging of Student Code: From Misconceptions to Contradictions and Updated Beliefs

arXiv: https://arxiv.org/abs/2511.00371 · HTML: https://mlsanity.com/paper/2511.00371/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 1 Nov 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Builds on (most cited first, 4 of 4)

- [MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors](https://mlsanity.com/paper/2502.18940/) (2025)
- [TeachLM: Post-Training LLMs for Education Using Authentic Learning Data](https://mlsanity.com/paper/2510.05087/) (2025)
- [LeanTutor: Towards a Verified AI Mathematical Proof Tutor](https://mlsanity.com/paper/2506.08321/) (2025)
- [McMining: Automated Discovery of Misconceptions in Student Code](https://mlsanity.com/paper/2510.08827/) (2025)

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

- [SocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair](https://mlsanity.com/paper/2610.15204/) (2026)

## Abstract (arXiv)

In Socratic debugging, instructors guide students towards identifying and fixing a bug on their own, instead of providing the bug fix directly. Most novice programmer bugs are caused by programming misconceptions, namely false beliefs about a programming concept. In this context, Socratic debugging can be formulated as a guided Reasoning Trajectory (RT) leading to a statement about the program behavior that contradicts the bug-causing misconception. Upon reaching this contradiction, the ensuing cognitive dissonance is expected to lead the student to identify the false belief on their own, followed by an enduring belief update. In this paper, we introduce the task of reasoning trajectory generation, together with a dataset of debugging problems annotated with RTs that are manually created or LLM-generated. We then describe LLM-based solutions for generating RTs and Socratic conversations that are anchored on them. A large-scale LLM-as-judge evaluation shows that large language and reasoning models can generate up to 91% correct reasoning trajectories and 98.7% valid conversation turns.