IntrEx: A Dataset for Modeling Engagement in Educational Conversations (2025.findings-emnlp)
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| Challenge: | IntrEx is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Approach: | They propose a large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Outcome: | The proposed dataset is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
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| Challenge: | Practicing conversations with large language models is a promising alternative to traditional in-person language learning. |
| Approach: | They propose a new token-level evaluation metric, Token Miss Rate, that measures the proportion of incomprehensible tokens per utterance and correlates strongly with human judgments. |
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Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems (2021.naacl-main)
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| Challenge: | Existing goal-oriented dialogue datasets focus on identifying slots and values, but in reality, customer service agents follow multi-step procedures derived from explicit company policies. |
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MEEP: Is this Engaging? Prompting Large Language Models for Dialogue Evaluation in Multilingual Settings (2023.findings-emnlp)
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| Challenge: | Existing metrics for engagingness evaluate the response without the conversation history, are designed for one dataset, or have limited correlation with human annotations. |
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| Challenge: | Existing models that measure engagement use expensive human annotas and abstract definitions of the term. |
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Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
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| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
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| Challenge: | Large language models (LLMs) are increasingly used as conversational partners for learning, yet the interactional dynamics supporting users’ learning and engagement are understudied. |
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EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)
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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang
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| Challenge: | Educational NLP systems are evaluated using engagement metrics and satisfaction surveys . authors identify a fundamental misalignment between pedagogical design and student usage patterns . |
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User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal (2025.emnlp-main)
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LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues (2024.naacl-srw)
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Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen
| Challenge: | Existing datasets with limited domain coverage and few challenging conversational phenomena are often unlabelled . Existing data is limited in quality and lacks a robust evaluation process . |
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