@inproceedings{wang-etal-2026-cat,
title = "{CAT}: A {C}ounter{A}rgument Tutoring System based on Logical Structure Analysis",
author = "Wang, Wenzhi and
Wambsganss, Thiemo and
Wang, Chenyang and
Reisert, Paul and
Wettstein, L{\'e}ane and
Pott, Katja and
Yamaguchi, Kenshi and
Shimmei, Machi and
Rietsche, Roman and
Pothong, Surawat and
Inoue, Naoya and
Inui, Kentaro",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.46/",
pages = "476--487",
ISBN = "979-8-89176-461-3",
abstract = "Engaging in counterarguments (CAs) has been shown to be a promising approach to effectively support the development of critical thinking skills in educational settings. Developing an argumentation tutoring system that automatically provides personalized feedback on CAs would benefit both teachers and students by mitigating the constraints imposed by limited instructional resources. However, the development of such systems remains underexplored. To address this gap, we develop CAT, a CounterArgument Tutoring system that analyzes the underlying logical structure of CAs and provides tailored feedback accordingly. To evaluate CAT, we conduct a user study with 39 undergraduate students over two weeks in a real classroom, comparing it against an existing argumentation tutoring system as a baseline. The results indicate that CAT supports students in critically evaluating opposing arguments, considering alternative perspectives, and refining their own reasoning. Statistical analysis further shows that CAT elicits more engaged interaction than the baseline system. Moreover, students perceive CAT as easy to use and valuable for improving both their critical thinking and argumentation skills."
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<abstract>Engaging in counterarguments (CAs) has been shown to be a promising approach to effectively support the development of critical thinking skills in educational settings. Developing an argumentation tutoring system that automatically provides personalized feedback on CAs would benefit both teachers and students by mitigating the constraints imposed by limited instructional resources. However, the development of such systems remains underexplored. To address this gap, we develop CAT, a CounterArgument Tutoring system that analyzes the underlying logical structure of CAs and provides tailored feedback accordingly. To evaluate CAT, we conduct a user study with 39 undergraduate students over two weeks in a real classroom, comparing it against an existing argumentation tutoring system as a baseline. The results indicate that CAT supports students in critically evaluating opposing arguments, considering alternative perspectives, and refining their own reasoning. Statistical analysis further shows that CAT elicits more engaged interaction than the baseline system. Moreover, students perceive CAT as easy to use and valuable for improving both their critical thinking and argumentation skills.</abstract>
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%0 Conference Proceedings
%T CAT: A CounterArgument Tutoring System based on Logical Structure Analysis
%A Wang, Wenzhi
%A Wambsganss, Thiemo
%A Wang, Chenyang
%A Reisert, Paul
%A Wettstein, Léane
%A Pott, Katja
%A Yamaguchi, Kenshi
%A Shimmei, Machi
%A Rietsche, Roman
%A Pothong, Surawat
%A Inoue, Naoya
%A Inui, Kentaro
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F wang-etal-2026-cat
%X Engaging in counterarguments (CAs) has been shown to be a promising approach to effectively support the development of critical thinking skills in educational settings. Developing an argumentation tutoring system that automatically provides personalized feedback on CAs would benefit both teachers and students by mitigating the constraints imposed by limited instructional resources. However, the development of such systems remains underexplored. To address this gap, we develop CAT, a CounterArgument Tutoring system that analyzes the underlying logical structure of CAs and provides tailored feedback accordingly. To evaluate CAT, we conduct a user study with 39 undergraduate students over two weeks in a real classroom, comparing it against an existing argumentation tutoring system as a baseline. The results indicate that CAT supports students in critically evaluating opposing arguments, considering alternative perspectives, and refining their own reasoning. Statistical analysis further shows that CAT elicits more engaged interaction than the baseline system. Moreover, students perceive CAT as easy to use and valuable for improving both their critical thinking and argumentation skills.
%U https://aclanthology.org/2026.emnlp-demos.46/
%P 476-487
Markdown (Informal)
[CAT: A CounterArgument Tutoring System based on Logical Structure Analysis](https://aclanthology.org/2026.emnlp-demos.46/) (Wang et al., EMNLP 2026)
ACL
- Wenzhi Wang, Thiemo Wambsganss, Chenyang Wang, Paul Reisert, Léane Wettstein, Katja Pott, Kenshi Yamaguchi, Machi Shimmei, Roman Rietsche, Surawat Pothong, Naoya Inoue, and Kentaro Inui. 2026. CAT: A CounterArgument Tutoring System based on Logical Structure Analysis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 476–487, Budapest, Hungary. Association for Computational Linguistics.