# Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure

arXiv: https://arxiv.org/abs/2410.03781 · HTML: https://mlsanity.com/paper/2410.03781/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 74 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Making Large Language Models Better Reasoners with Step-Aware Verifier](https://mlsanity.com/paper/2206.02336/) (2022)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023)
- [Solving Math Word Problems by Combining Language Models With Symbolic Solvers](https://mlsanity.com/paper/2304.09102/) (2023)
- [The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues](https://mlsanity.com/paper/2205.07540/) (2022)
- [Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approach](https://mlsanity.com/paper/2407.12687/) (2024)
- [Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes](https://mlsanity.com/paper/2310.10648/) (2023)
- [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023)
- [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://mlsanity.com/paper/2211.12835/) (2022)
- [Learning gain differences between ChatGPT and human tutor generated algebra hints](https://mlsanity.com/paper/2302.06871/) (2023)
- [Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors](https://mlsanity.com/paper/2407.09136/) (2024)
- [Opportunities and Challenges in Neural Dialog Tutoring](https://mlsanity.com/paper/2301.09919/) (2023)
- [AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails](https://mlsanity.com/paper/2402.09216/) (2024)
- [The ART of LLM Refinement: Ask, Refine, and Trust](https://mlsanity.com/paper/2311.07961/) (2023)
- [Book2Dial: Generating Teacher-Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots](https://mlsanity.com/paper/2403.03307/) (2024)
- [Chain of Thought Prompting Elicits Knowledge Augmentation](https://mlsanity.com/paper/2307.01640/) (2023)

## Built on by (most cited first, 24 of 24)

- [From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning](https://mlsanity.com/paper/2505.15607/) (2025)
- [Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks](https://mlsanity.com/paper/2604.18660/) (2026)
- [ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations](https://mlsanity.com/paper/2506.14200/) (2025)
- [PATS: Personality-Aware Teaching Strategies with Large Language Model Tutors](https://mlsanity.com/paper/2601.08402/) (2026)
- [Letting Tutor Personas Speak Up for LLMs: Learning Steering Vectors from Dialogue via Preference Optimization](https://mlsanity.com/paper/2602.07639/) (2026)
- [EduVidQA: Generating and Evaluating Long-form Answers to Student Questions based on Lecture Videos](https://mlsanity.com/paper/2509.24120/) (2025)
- [Readability Reconsidered: A Cross-Dataset Analysis of Reference-Free Metrics](https://mlsanity.com/paper/2510.15345/) (2025)
- [Pedagogy-R1: Pedagogically-Aligned Reasoning Model with Balanced Educational Benchmark](https://mlsanity.com/paper/2505.18467/) (2025)
- [BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation](https://mlsanity.com/paper/2602.13280/) (2026)
- [Confidence Should Be Calibrated More Than One Turn Deep](https://mlsanity.com/paper/2604.05397/) (2026)
- [Uncertainty-Aware Generation and Decision-Making Under Ambiguity](https://mlsanity.com/paper/2606.30578/) (2026)
- [The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory](https://mlsanity.com/paper/2503.10533/) (2025)
- [Do Large Language Models Mentalize When They Teach?](https://mlsanity.com/paper/2604.01594/) (2026)
- [Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning](https://mlsanity.com/paper/2604.06385/) (2026)
- [Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education](https://mlsanity.com/paper/2605.15850/) (2026)
- [Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation](https://mlsanity.com/paper/2606.01020/) (2026)
- [Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems](https://mlsanity.com/paper/2606.08030/) (2026)
- [Mitigating Scaffolding Collapse in Socratic Tutors via Representation Alignment](https://mlsanity.com/paper/2607.19371/) (2026)
- [Auditable Release Control for Pedagogical Leakage in LLM Tutors](https://mlsanity.com/paper/2608.00515/) (2026)
- [PIVOT: Preference-based Intervention Vectors for Pedagogical Tutor Steering](https://mlsanity.com/paper/2608.07509/) (2026)
- [DICE: Decoupling Capability from Intervention Necessity in LLM Tutoring](https://mlsanity.com/paper/2610.04825/) (2026)
- [Sherpa: Teaching LLMs to Teach Adaptively](https://mlsanity.com/paper/2610.08778/) (2026)
- [Do LLM Tutors Actually Teach? A Process-Centered Synthesis of the Tutoring Loop](https://mlsanity.com/paper/2610.15692/) (2026)
- [Online LLM Personalization from Pairwise Preferences Across Users](https://mlsanity.com/paper/2610.17203/) (2026)

## Abstract (arXiv)

One-to-one tutoring is one of the most efficient methods of teaching. With the growing popularity of Large Language Models (LLMs), there have been efforts to create LLM based conversational tutors which can expand the benefits of one to one tutoring to everyone. However, current LLMs are trained primarily to be helpful assistants and lack crucial pedagogical skills. For example, they often quickly reveal the solution to the student and fail to plan for a richer multi turn pedagogical interaction. To use LLMs in pedagogical settings, they need to be steered to use effective teaching strategies: a problem we introduce as Pedagogical Steering. We develop StratL, an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. As a case study, we create a prototype tutor for high school math following Productive Failure (PF), an advanced and effective learning design. To validate our approach in a real-world setting, we run a field study with 17 high school students in Singapore and show that StratL succeeds in steering the LLM to follow the PF tutoring strategy. Finally, we highlight challenges in Pedagogical Steering of LLMs and offer opportunities for further improvements by publishing a dataset of PF problems and our code.