Papers by Manu Kapur
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems (2022.emnlp-main)
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| Challenge: | We hypothesize that questioning can enhance human performance and assist solvers . |
| Approach: | They propose to use large language models to generate sequential questions for math word problem-solving . they propose to apply these models to a variety of math word problems . |
| Outcome: | The proposed model improves the performance of a math word problem solver by generating more questions than other models. |
Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure (2025.findings-acl)
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| Challenge: | Recent studies have shown that LLMs are not able to provide one-to-one tutoring solutions because of their high cost and efficiency. |
| Approach: | They propose an algorithm to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. |
| Outcome: | The proposed algorithm is able to optimize LLM prompts and steer it to follow a predefined multi-turn tutoring plan represented as a transition graph. |
MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors (2025.emnlp-main)
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| Challenge: | Evaluating the pedagogical capabilities of AI-based tutoring models is critical for guided progress in the field. |
| Approach: | They propose an open-source benchmark for holistic tutoring model evaluation. |
| Outcome: | The proposed model can discriminate between expert and novice teachers with high accuracy. |
MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems (2023.findings-emnlp)
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Jakub Macina, Nico Daheim, Sankalan Chowdhury, Tanmay Sinha, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan
| Challenge: | Existing models for automatic dialogue tutoring fail to provide accurate feedback or reveal solutions to students too early. |
| Approach: | They propose a framework to generate one-to-one teacher-student tutoring dialogues by pairing human teachers with a Large Language Model (LLM) they use scaffolding questions and annotations to fine-tune models to be more effective tutors . |
| Outcome: | The proposed framework can generate 3k one-to-one teacher-student tutoring dialogues grounded in multi-step math reasoning problems. |
Opportunities and Challenges in Neural Dialog Tutoring (2023.eacl-main)
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| Challenge: | Existing approaches to designing dialog tutors have been challenging . current approaches perform poorly in constrained learning scenarios, authors find . |
| Approach: | They analyze dialog tutoring models using automatic and human evaluations to understand the new opportunities brought by dialog tutors. |
| Outcome: | The proposed models perform poorly in less constrained learning scenarios, the authors show . they find large number of model reasoning errors in 45% of conversations . |
Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors (2024.emnlp-main)
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| Challenge: | Existing models for dialog tutoring fail to detect student errors and tailor their feedback to them. |
| Approach: | They propose to build dialog tutoring models to scaffold students' problem-solving and verify student solutions by using automatic and human evaluation. |
| Outcome: | The proposed model improves the quality of the tutor response generation by detecting student errors and adjusting the feedback to the errors. |