# Book2Dial: Generating Teacher-Student Interactions from Textbooks for Cost-Effective Development of Educational Chatbots

arXiv: https://arxiv.org/abs/2403.03307 · HTML: https://mlsanity.com/paper/2403.03307/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 31 of its 65 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 30 of 31)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [QuestEval: Summarization Asks for Fact-based Evaluation](https://mlsanity.com/paper/2103.12693/) (2021)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://mlsanity.com/paper/2212.10465/) (2022)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Q2: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://mlsanity.com/paper/2104.08202/) (2021)
- [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023)
- [Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2005.11364/) (2020)
- [PLACES: Prompting Language Models for Social Conversation Synthesis](https://mlsanity.com/paper/2302.03269/) (2023)
- [Dialog Inpainting: Turning Documents into Dialogs](https://mlsanity.com/paper/2205.09073/) (2022)
- [Evaluating Coherence in Dialogue Systems using Entailment](https://mlsanity.com/paper/1904.03371/) (2019)
- [CLASS: A Design Framework for building Intelligent Tutoring Systems based on Learning Science principles](https://mlsanity.com/paper/2305.13272/) (2023)
- [Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions](https://mlsanity.com/paper/2106.03873/) (2021)
- [The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts](https://mlsanity.com/paper/2211.11772/) (2022)
- [Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://mlsanity.com/paper/2211.12835/) (2022)
- [Opportunities and Challenges in Neural Dialog Tutoring](https://mlsanity.com/paper/2301.09919/) (2023)
- [Elastic Weight Removal for Faithful and Abstractive Dialogue Generation](https://mlsanity.com/paper/2303.17574/) (2023)
- [Synthetic data generation for end-to-end thermal infrared tracking](https://mlsanity.com/paper/1806.01013/) (2018)
- [Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2004.14530/) (2020)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://mlsanity.com/paper/2211.01482/) (2022)
- [Ask to Learn: A Study on Curiosity-driven Question Generation](https://mlsanity.com/paper/1911.03350/) (2019)
- [Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources](https://mlsanity.com/paper/2311.07589/) (2023)
- [Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification](https://mlsanity.com/paper/2304.05578/) (2023)

## Built on by (most cited first, 25 of 25)

- [MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors](https://mlsanity.com/paper/2502.18940/) (2025)
- [Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors](https://mlsanity.com/paper/2407.09136/) (2024)
- [Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failure](https://mlsanity.com/paper/2410.03781/) (2024)
- [Simulated Students in Tutoring Dialogues: Substance or Illusion?](https://mlsanity.com/paper/2601.04025/) (2026)
- [One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction](https://mlsanity.com/paper/2502.12633/) (2025)
- [From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning](https://mlsanity.com/paper/2505.15607/) (2025)
- [CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues](https://mlsanity.com/paper/2404.03820/) (2024)
- [Token Weighting for Long-Range Language Modeling](https://mlsanity.com/paper/2503.09202/) (2025)
- [Revealing and Mitigating the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing](https://mlsanity.com/paper/2409.11726/) (2024)
- [Position: LLMs Can be Good Tutors in English Education](https://mlsanity.com/paper/2502.05467/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use](https://mlsanity.com/paper/2410.19231/) (2024)
- [Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models](https://mlsanity.com/paper/2506.03735/) (2025)
- [SEFL: A Framework for Generating Synthetic Educational Assignment Feedback with LLM Agents](https://mlsanity.com/paper/2502.12927/) (2025)
- [Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues](https://mlsanity.com/paper/2605.30051/) (2026)
- [DocTalk: Scalable Graph-based Dialogue Synthesis for Enhancing LLM Conversational Capabilities](https://mlsanity.com/paper/2507.05750/) (2025)
- [HACHIMI: Scalable and Controllable Student Persona Generation via Orchestrated Agents](https://mlsanity.com/paper/2603.04855/) (2026)
- [SID: Benchmarking Guided Instruction Capabilities in STEM Education with a Socratic Interdisciplinary Dialogues Dataset](https://mlsanity.com/paper/2508.04563/) (2025)
- [A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents](https://mlsanity.com/paper/2601.10809/) (2026)
- [EduDial: Constructing a Large-scale Multi-turn Teacher-Student Dialogue Corpus](https://mlsanity.com/paper/2510.12899/) (2025)
- [FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing](https://mlsanity.com/paper/2511.22883/) (2025)
- [Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation](https://mlsanity.com/paper/2606.01020/) (2026)
- [GeoDial: A Multimodal Conversational Tutoring Dataset for Geometry Problem-Solving with Visual Tutor Turns](https://mlsanity.com/paper/2606.12419/) (2026)
- [From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions](https://mlsanity.com/paper/2606.18257/) (2026)
- [PIVOT: Preference-based Intervention Vectors for Pedagogical Tutor Steering](https://mlsanity.com/paper/2608.07509/) (2026)

## Abstract (arXiv)

Educational chatbots are a promising tool for assisting student learning. However, the development of effective chatbots in education has been challenging, as high-quality data is seldom available in this domain. In this paper, we propose a framework for generating synthetic teacher-student interactions grounded in a set of textbooks. Our approaches capture one aspect of learning interactions where curious students with partial knowledge interactively ask a teacher questions about the material in the textbook. We highlight various quality criteria that such dialogues should fulfill and compare several approaches relying on either prompting or fine-tuning large language models. We use synthetic dialogues to train educational chatbots and show benefits of further fine-tuning in different educational domains. However, human evaluation shows that our best data synthesis method still suffers from hallucinations and tends to reiterate information from previous conversations. Our findings offer insights for future efforts in synthesizing conversational data that strikes a balance between size and quality. We will open-source our data and code.