Curriculum-Driven Edubot: A Framework for Developing Language Learning Chatbots Through Synthesizing Conversational Data
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Abstract (arXiv)
Chatbots have become popular in educational settings, revolutionizing how students interact with material and how teachers teach. We present Curriculum-Driven EduBot, a framework for developing a chatbot that combines the interactive features of chatbots with the systematic material of English textbooks to assist students in enhancing their conversational skills. We begin by extracting pertinent topics from textbooks and using large language models to generate dialogues related to these topics. We then fine-tune an open-source model using our generated conversational data to create our curriculum-driven chatbot. User studies demonstrate that EduBot outperforms ChatGPT in leading curriculum-based dialogues and adapting its dialogue to match the user's English proficiency level. By combining traditional textbook methodologies with conversational AI, our approach offers learners an interactive tool that aligns with their curriculum and provides user-tailored conversation practice. This facilitates meaningful student-bot dialogues and enriches the overall learning experience within the curriculum's pedagogical framework.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 28 Sep 2023.
Builds on
22 of 46 references.
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10 more
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Citing papers per year: 2024: 6; 2025: 2; 2026: 1.
9 papers.
- 2024Language Models as Science Tutorscited by 17
- 2024The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Educationcited by 9
- 2024Using Adaptive Empathetic Responses for Teaching Englishcited by 5
- 2024EDEN: Empathetic Dialogues for English learningcited by 5
- 2024"Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baselinecited by 1
- 2024YA-TA: Towards Personalized Question-Answering Teaching Assistants using Instructor-Student Dual Retrieval-augmented Knowledge Fusioncited by 1
- 2025Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants' Question-Answering in Asynchronous Learning Environmentscited by 1
- 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agentscited by 1
- 2025Is your multimodal large language model a good science tutor?cited by 0
- 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents15 Jan 2026
- 2025Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants' Question-Answering in Asynchronous Learning Environments22 Sep 2025
- 2025Is your multimodal large language model a good science tutor?9 May 2025
- 2024YA-TA: Towards Personalized Question-Answering Teaching Assistants using Instructor-Student Dual Retrieval-augmented Knowledge Fusion31 Aug 2024
- 2024"Is ChatGPT a Better Explainer than My Professor?": Evaluating the Explanation Capabilities of LLMs in Conversation Compared to a Human Baseline26 Jun 2024
- 2024EDEN: Empathetic Dialogues for English learning25 Jun 2024
- 2024Using Adaptive Empathetic Responses for Teaching English21 Apr 2024
- 2024The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education3 Apr 2024
- 2024Language Models as Science Tutors16 Feb 2024