| Challenge: | Existing Large Language Models (LLMs) are limited in scope and lack pedagogical depth. |
| Approach: | They construct a BIlingual PEDagogically-informed Tutoring Dataset of one-on-one, human-to-human tutoring interactions using a post-hoc analysis. |
| Outcome: | The proposed models replicate the style of human teachers and employ diverse and contextually appropriate pedagogical strategies. |
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Jingheng Ye, Shen Wang, Deqing Zou, Yibo Yan, Kun Wang, Hai-Tao Zheng, Ruitong Liu, Zenglin Xu, Irwin King, Philip S. Yu, Qingsong Wen
| Challenge: | Recent efforts to integrate large language models into English education lack adaptability to language learning. |
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Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors (2025.naacl-long)
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| Challenge: | Existing evaluations of large language models have been limited to subjective protocols and benchmarks. |
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MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) are often optimized for direct question-answering, but their effectiveness is often undermined by strategically withholding answers. |
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Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee
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Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System (N19-2)
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Shazia Afzal, Tejas Dhamecha, Nirmal Mukhi, Renuka Sindhgatta, Smit Marvaniya, Matthew Ventura, Jessica Yarbro
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Self-chats from Large Language Models Make Small Emotional Support Chatbot Better (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. |
| Approach: | They propose an iterative expansion framework to prompt large teacher model to curate an expansive emotion support dialogue dataset. |
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Can Large Language Models Be Good Language Teachers? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have achieved remarkable success across diverse domains, but their potential as effective language teachers remains inadequately assessed. |
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