@inproceedings{61234,
  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.}},
  author       = {{Fichtel, Leandra and Spliethöver, Maximilian and Hüllermeier, 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}},
  booktitle    = {{Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Investigating Co-Constructive Behavior of Large Language Models in  Explanation Dialogues}}},
  year         = {{2025}},
}

@book{61178,
  editor       = {{Ilinykh, Nikolai and Robrecht, Amelie and Kopp, Stefan and Buschmeier, Hendrik}},
  issn         = {{2308-2275}},
  location     = {{Bielefeld, Germany}},
  pages        = {{271+viii}},
  title        = {{{SemDial 2025 – Bialogue. Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue}}},
  year         = {{2025}},
}

@inproceedings{61418,
  author       = {{Robrecht, Amelie and Kopp, Stefan}},
  booktitle    = {{Proceedings of the KogWis 2025}},
  title        = {{{A Computational Approach to Adaptive Explanation Generation Based on Cognitive Partner Models}}},
  year         = {{2025}},
}

@unpublished{55154,
  abstract     = {{In human interaction, gestures serve various functions such as marking speech
rhythm, highlighting key elements, and supplementing information. These
gestures are also observed in explanatory contexts. However, the impact of
gestures on explanations provided by virtual agents remains underexplored. A
user study was carried out to investigate how different types of gestures
influence perceived interaction quality and listener understanding. This study
addresses the effect of gestures in explanation by developing an embodied
virtual explainer integrating both beat gestures and iconic gestures to enhance
its automatically generated verbal explanations. Our model combines beat
gestures generated by a learned speech-driven synthesis module with manually
captured iconic gestures, supporting the agent's verbal expressions about the
board game Quarto! as an explanation scenario. Findings indicate that neither
the use of iconic gestures alone nor their combination with beat gestures
outperforms the baseline or beat-only conditions in terms of understanding.
Nonetheless, compared to prior research, the embodied agent significantly
enhances understanding.}},
  author       = {{Robrecht, Amelie and Voss, Hendric and Gottschalk, Lisa and Kopp, Stefan}},
  booktitle    = {{arXiv:2406.12544}},
  title        = {{{Integrating Representational Gestures into Automatically Generated  Embodied Explanations and its Effects on Understanding and Interaction  Quality}}},
  year         = {{2024}},
}

@inproceedings{61417,
  author       = {{Robrecht, Amelie and Buhl, Heike M. and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 28th Workshop on the Semantics and Pragmatics of Dialogue }},
  title        = {{{Inferring Partner Models for Adaptive Explanation Generation}}},
  year         = {{2024}},
}

@inproceedings{55155,
  author       = {{Robrecht, Amelie and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 15th International Conference on Agents and Artificial Intelligence}},
  publisher    = {{SCITEPRESS - Science and Technology Publications}},
  title        = {{{SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation}}},
  doi          = {{10.5220/0011671300003393}},
  year         = {{2023}},
}

@inproceedings{55152,
  author       = {{Robrecht, Amelie and Rothgänger, Markus and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents}},
  publisher    = {{ACM}},
  title        = {{{A Study on the Benefits and Drawbacks of Adaptivity in AI-generated Explanations}}},
  doi          = {{10.1145/3570945.3607339}},
  year         = {{2023}},
}

@inproceedings{51367,
  author       = {{Robrecht, Amelie and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 15th International Conference on Agents and Artificial Intelligence}},
  isbn         = {{978-989-758-623-1}},
  location     = {{Lisbon}},
  pages        = {{48--58}},
  publisher    = {{SCITEPRESS - Science and Technology Publications}},
  title        = {{{SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation}}},
  doi          = {{10.5220/0011671300003393}},
  year         = {{2023}},
}

@inproceedings{55156,
  author       = {{Fisher, Josephine Beryl and Robrecht, Amelie and Kopp, Stefan and Rohlfing, Katharina J.}},
  booktitle    = {{Proceedings of the 27th Workshop on the Semantics and Pragmatics of Dialogue }},
  location     = {{Maribor}},
  title        = {{{Exploring the Semantic Dialogue Patterns of Explanations – a Case Study of Game Explanations}}},
  year         = {{2023}},
}