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    <abstract>Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLMs are increasingly recognized as key technology for advancing intelligent education, owing to their ability to deeply understand instructional contexts and provide personalized guidance, constructing dedicated teacher-student dialogue benchmarks has become particularly important. To this end, we present EduDial, a comprehensive multi-turn teacher-student dialogue dataset. EduDial covers 345 core knowledge points and consists of 34,250 dialogue sessions from interactions between teacher and student agents. Its design is guided by Bloom’s taxonomy of educational objectives and incorporates ten questioning strategies—including situational questioning, zone of proximal development (ZPD) questioning, and metacognitive questioning—better capturing authentic classroom interactions. Furthermore, we design differentiated teaching strategies for students at different cognitive levels, providing more targeted guidance. Building on EduDial, we develop EduDial-LLM 32B via training and propose an 11-dimensional evaluation framework that systematically measures the teaching abilities of LLMs, encompassing overall teaching quality and content quality. Experiments on 17 mainstream LLMs reveal that most models struggle in student-centered teaching scenarios, whereas EduDial-LLM achieves significant gains, consistently outperforming all baselines across all metrics. Code is available at https://github.com/ECNU-RAIL/EduDial-EMNLP2026.</abstract>
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%0 Conference Proceedings
%T EduDial: Constructing a Large-scale Multi-turn Teacher–Student Dialogue Corpus
%A Wei, Shouang
%A Zhang, Min
%A Lin, Xin
%A Jiang, Bo
%A Dai, Zhongxiang
%A Kuang, Kun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wei-etal-2026-edudial
%X Recently, several multi-turn dialogue benchmarks have been proposed to evaluate the conversational abilities of large language models (LLMs). As LLMs are increasingly recognized as key technology for advancing intelligent education, owing to their ability to deeply understand instructional contexts and provide personalized guidance, constructing dedicated teacher-student dialogue benchmarks has become particularly important. To this end, we present EduDial, a comprehensive multi-turn teacher-student dialogue dataset. EduDial covers 345 core knowledge points and consists of 34,250 dialogue sessions from interactions between teacher and student agents. Its design is guided by Bloom’s taxonomy of educational objectives and incorporates ten questioning strategies—including situational questioning, zone of proximal development (ZPD) questioning, and metacognitive questioning—better capturing authentic classroom interactions. Furthermore, we design differentiated teaching strategies for students at different cognitive levels, providing more targeted guidance. Building on EduDial, we develop EduDial-LLM 32B via training and propose an 11-dimensional evaluation framework that systematically measures the teaching abilities of LLMs, encompassing overall teaching quality and content quality. Experiments on 17 mainstream LLMs reveal that most models struggle in student-centered teaching scenarios, whereas EduDial-LLM achieves significant gains, consistently outperforming all baselines across all metrics. Code is available at https://github.com/ECNU-RAIL/EduDial-EMNLP2026.
%U https://aclanthology.org/2026.findings-emnlp.148/
%P 3113-3134

Markdown (Informal)

[EduDial: Constructing a Large-scale Multi-turn Teacher–Student Dialogue Corpus](https://aclanthology.org/2026.findings-emnlp.148/) (Wei et al., Findings 2026)

• EduDial: Constructing a Large-scale Multi-turn Teacher–Student Dialogue Corpus (Wei et al., Findings 2026)

ACL

• Shouang Wei, Min Zhang, Xin Lin, Bo Jiang, Zhongxiang Dai, and Kun Kuang. 2026. EduDial: Constructing a Large-scale Multi-turn Teacher–Student Dialogue Corpus. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 3113–3134, Budapest, Hungary. Association for Computational Linguistics.