@inbook{65084,
  author       = {{Buhl, Heike M. and Vollmer, Anna-Lisa and Alami, Rachid and Booshehri, Meisam and Främling, Kary}},
  booktitle    = {{Social explainable AI}},
  editor       = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kisten}},
  pages        = {{269--295}},
  publisher    = {{Springer}},
  title        = {{{Models of the situation, the explanandum, and the interaction partner}}},
  doi          = {{https://doi.org/10.1007/978-981-96-5290-7_14}},
  year         = {{2026}},
}

@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}},
}

@article{61156,
  abstract     = {{Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) ‘understanding’ on the part of the explainee. However, what it means to ‘understand’ is still not clearly defined, and the concept itself is rarely the subject of scientific investigation. This conceptual article aims to present a model of forms of understanding for XAI-explanations and beyond. From an interdisciplinary perspective bringing together computer science, linguistics, sociology, philosophy and psychology, a definition of understanding and its forms, assessment, and dynamics during the process of giving everyday explanations are explored. Two types of understanding are considered as possible outcomes of explanations, namely enabledness, ‘knowing how’ to do or decide something, and comprehension, ‘knowing that’ – both in different degrees (from shallow to deep). Explanations regularly start with shallow understanding in a specific domain and can lead to deep comprehension and enabledness of the explanandum, which we see as a prerequisite for human users to gain agency. In this process, the increase of comprehension and enabledness are highly interdependent. Against the background of this systematization, special challenges of understanding in XAI are discussed.}},
  author       = {{Buschmeier, Hendrik and Buhl, Heike M. and Kern, Friederike and Grimminger, Angela and Beierling, Helen and Fisher, Josephine Beryl and Groß, André and Horwath, Ilona and Klowait, Nils and Lazarov, Stefan Teodorov and Lenke, Michael and Lohmer, Vivien and Rohlfing, Katharina and Scharlau, Ingrid and Singh, Amit and Terfloth, Lutz and Vollmer, Anna-Lisa and Wang, Yu and Wilmes, Annedore and Wrede, Britta}},
  journal      = {{Cognitive Systems Research}},
  keywords     = {{understanding, explaining, explanations, explainable, AI, interdisciplinarity, comprehension, enabledness, agency}},
  title        = {{{Forms of Understanding for XAI-Explanations}}},
  doi          = {{10.1016/j.cogsys.2025.101419}},
  volume       = {{94}},
  year         = {{2025}},
}

@inproceedings{55403,
  abstract     = {{In this paper we consider the interactive processes by which an explainer and an explainee cooperate to produce an explanation, which we refer to as co-construction. Explainable Artificial Intelligence (XAI) is concerned with the development of intelligent systems and robots that can explain and justify their actions, decisions, recommendations, and so on. However, the cooperative construction of explanations remains a key but under-explored issue. This short paper proposes an architecture for intelligent systems that promotes a co-constructive and interactive approach to explanation generation. By outlining its basic components and their specific roles, we aim to contribute to the advancement of XAI computational frameworks that actively engage users in the explanation process.}},
  author       = {{Buschmeier, Hendrik and Cimiano, Philipp and Kopp, Stefan and Kornowicz, Jaroslaw and Lammert, Olesja and Matarese, Marco and Mindlin, Dimitry and Robrecht, Amelie Sophie and Vollmer, Anna-Lisa and Wagner, Petra and Wrede, Britta and Booshehri, Meisam}},
  booktitle    = {{Proceedings of the 2024 Workshop on Explainability Engineering}},
  location     = {{Lisbon, Portugal}},
  pages        = {{20--25}},
  publisher    = {{ACM}},
  title        = {{{Towards a Computational Architecture for Co-Constructive Explainable Systems}}},
  doi          = {{10.1145/3648505.3648509}},
  year         = {{2024}},
}