# BIPED: Pedagogically Informed Tutoring System for ESL Education

arXiv: https://arxiv.org/abs/2406.03486 · HTML: https://mlsanity.com/paper/2406.03486/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves](https://mlsanity.com/paper/2204.09652/) (2022)
- [Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling](https://mlsanity.com/paper/2403.14071/) (2024)
- [Opportunities and Challenges in Neural Dialog Tutoring](https://mlsanity.com/paper/2301.09919/) (2023)
- [Computationally Identifying Funneling and Focusing Questions in Classroom Discourse](https://mlsanity.com/paper/2208.04715/) (2022)
- [Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation](https://mlsanity.com/paper/2302.13496/) (2023)
- [User Adaptive Language Learning Chatbots with a Curriculum](https://mlsanity.com/paper/2304.05489/) (2023)
- [Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification](https://mlsanity.com/paper/2304.05578/) (2023)
- [Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets](https://mlsanity.com/paper/2304.07499/) (2023)

## Built on by (most cited first, 7 of 7)

- [Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutors](https://mlsanity.com/paper/2407.09136/) (2024)
- [Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education](https://mlsanity.com/paper/2502.05863/) (2025)
- [SEFL: A Framework for Generating Synthetic Educational Assignment Feedback with LLM Agents](https://mlsanity.com/paper/2502.12927/) (2025)
- [AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI](https://mlsanity.com/paper/2603.26679/) (2026)
- [TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring](https://mlsanity.com/paper/2608.03952/) (2026)
- [Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks](https://mlsanity.com/paper/2608.17919/) (2026)
- [CoLearn: An Agentic Tutor that Learns its Learner in a Human--AI Co-Learning Loop](https://mlsanity.com/paper/2609.21154/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have a great potential to serve as readily available and cost-efficient Conversational Intelligent Tutoring Systems (CITS) for teaching L2 learners of English. Existing CITS, however, are designed to teach only simple concepts or lack the pedagogical depth necessary to address diverse learning strategies. To develop a more pedagogically informed CITS capable of teaching complex concepts, we construct a BIlingual PEDagogically-informed Tutoring Dataset (BIPED) of one-on-one, human-to-human English tutoring interactions. Through post-hoc analysis of the tutoring interactions, we come up with a lexicon of dialogue acts (34 tutor acts and 9 student acts), which we use to further annotate the collected dataset. Based on a two-step framework of first predicting the appropriate tutor act then generating the corresponding response, we implemented two CITS models using GPT-4 and SOLAR-KO, respectively. We experimentally demonstrate that the implemented models not only replicate the style of human teachers but also employ diverse and contextually appropriate pedagogical strategies.