@inproceedings{fichtel-etal-2025-investigating,
title = "Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues",
author = {Fichtel, Leandra and
Splieth{\"o}ver, Maximilian and
H{\"u}llermeier, Eyke and
Jimenez, Patricia and
Klowait, Nils and
Kopp, Stefan and
Ngonga Ngomo, Axel-Cyrille and
Robrecht, Amelie and
Scharlau, Ingrid and
Terfloth, Lutz and
Vollmer, Anna-Lisa and
Wachsmuth, Henning},
editor = "B{\'e}chet, Fr{\'e}d{\'e}ric and
Lef{\`e}vre, Fabrice and
Asher, Nicholas and
Kim, Seokhwan and
Merlin, Teva",
booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2025",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sigdial-1.1/",
pages = "1--20",
abstract = "The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee{'}s background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee{'}s understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees' understanding before and after the dialogue, as well as their perception of the LLMs' co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees' engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited."
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<abstract>The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee’s background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee’s understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees’ understanding before and after the dialogue, as well as their perception of the LLMs’ co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees’ engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited.</abstract>
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%0 Conference Proceedings
%T Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
%A Fichtel, Leandra
%A Spliethöver, Maximilian
%A Hüllermeier, Eyke
%A Jimenez, Patricia
%A Klowait, Nils
%A Kopp, Stefan
%A Ngonga Ngomo, Axel-Cyrille
%A Robrecht, Amelie
%A Scharlau, Ingrid
%A Terfloth, Lutz
%A Vollmer, Anna-Lisa
%A Wachsmuth, Henning
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F fichtel-etal-2025-investigating
%X The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee’s background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee’s understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees’ understanding before and after the dialogue, as well as their perception of the LLMs’ co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees’ engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited.
%U https://aclanthology.org/2025.sigdial-1.1/
%P 1-20
Markdown (Informal)
[Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues](https://aclanthology.org/2025.sigdial-1.1/) (Fichtel et al., SIGDIAL 2025)
ACL
- Leandra Fichtel, Maximilian Spliethöver, Eyke Hüllermeier, Patricia Jimenez, Nils Klowait, Stefan Kopp, Axel-Cyrille Ngonga Ngomo, Amelie Robrecht, Ingrid Scharlau, Lutz Terfloth, Anna-Lisa Vollmer, and Henning Wachsmuth. 2025. Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues. In Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 1–20, Avignon, France. Association for Computational Linguistics.