Components of an explanation for co-constructive sXAI - Vollmer, Anna-Lisa; Buhl, Heike M.; Alami, Rachid; Främling, Kary; Grimminger, Angela; Booshehri, Meisam; Ngonga Ngomo, Axel-Cyrille (Social Explainable AI)

Models of the situation, the explanandum, and the interaction partner - Buhl, Heike M.; Vollmer, Anna-Lisa; Alami, Rachid; Booshehri, Meisam; Främling, Kary (Social explainable AI)

Adaptation - Buhl, Heike M.; Wrede, Britta; Fisher, Josephine Beryl; Matarese, Marco (Social Explainable AI)

Bridging the Dual Nature: How Integrated Explanations Enhance Understanding of Technical Artifacts - Terfloth, Lutz; Buhl, Heike M.; Lohmer, Vivien; Schaffer, Michael; Kern, Frederike; Schulte, Carsten

Retrospective video recall for analyzing cognitive processes in naturalistic explanations - Lazarov, Stefan Teodorov; Schaffer, Michael; Gladow, Viviane; Buschmeier, Hendrik; Buhl, Heike M.; Grimminger, Angela
In this paper, we discuss the application of retrospective video recall for the assessment of cognitive processes in explanatory interactions, such as understanding and mental models. Our purpose is to reflect on the benefits and limitations of video recall compared to another self-report method, ‘thinking-aloud’. To do so, we reveal empirical results from the application of video recall in three interdisciplinary research projects that applied the method for the qualitative and quantitative assessment of cognitive and behavioral phenomena in everyday explanations. In all three projects, video recall was applied as a post-hoc procedure following the recording of dyadic face-to-face explanations of board games. The design of the video recall procedure differed between individual projects because they pursued different research objectives – that is the investigation of (1) an interlocutor's multimodal signals of understanding, (2) the change in assumptions about an interlocutor's dispositional and situational knowledge, and (3) the differentiated assessment of an interlocutor's developing understanding of domain knowledge aspects by distinguishing between mechanistic and functional explanatory stances. By discussing the benefits and the limitations of each procedure, this article provides critical reflections on video recall as a versatile research method applied for the analysis of human multimodal behavior in interaction and cognitive processing.

Navigating the dual nature: do explainers adapt to explainee interests when explaining technical artifacts - Lutz, Terfloth; Buhl, Heike M.; Lohmer, Vivien; Kern, Friederike; Schaffer, Michael E.; Schulte, Carsten (International Journal of Technology and Design Education)

Generative Künstliche Intelligenz im Kontext von Lehren, Lernen und Beraten: Eine Perspektive der Pädagogischen Psychologie - Scheiter, Katharina; Bach, Katharina M.; Bauer, Elisabeth; Brod, Garvin; Bromme, Rainer; Buhl, Heike M.; Gagl, Benjamin; Greiff, Samuel; Hefter, Markus H.; Janke, Stefan; Lindner, Marlit A.; Mayweg, Elisabeth; et al. (Psychologische Rundschau)

Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues - Fichtel, Leandra; Spliethöver, Maximilian; Hüllermeier, Eyke; Jimenez, Patricia; Klowait, Nils; Kopp, Stefan; Ngonga Ngomo, Axel-Cyrille; Robrecht, Amelie; Scharlau, Ingrid; Terfloth, Lutz; Vollmer, Anna-Lisa; Wachsmuth, Henning (Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue)
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.

The Dual Nature as a Local Context to Explore Verbal Behaviour in Game Explanations - Fisher, Josephine Beryl; Terfloth, Lutz (Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2025))

Explanation needs and ethical demands: unpacking the instrumental value of XAI - Alpsancar, Suzana; Buhl, Heike M.; Matzner, Tobias; Scharlau, Ingrid (AI and Ethics)

Forms of Understanding for XAI-Explanations - Buschmeier, Hendrik; Buhl, Heike M.; Kern, Friederike; Grimminger, Angela; Beierling, Helen; Fisher, Josephine Beryl; Groß, André; Horwath, Ilona; Klowait, Nils; Lazarov, Stefan Teodorov; Lenke, Michael; Lohmer, Vivien; et al. (Cognitive Systems Research)
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.
Keywords: understanding; explaining; explanations; explainable; AI; interdisciplinarity; comprehension; enabledness; agency

Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues - Fichtel, Leandra; Spliethöver, Maximilian; Hüllermeier, Eyke; Jimenez, Patricia; Klowait, Nils; Kopp, Stefan; Ngonga Ngomo, Axel-Cyrille; Robrecht, Amelie; Scharlau, Ingrid; Terfloth, Lutz; Vollmer, Anna-Lisa; Wachsmuth, Henning (arXiv:2504.18483)
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.

The role of interactive gestures in explanatory interactions - Lohmer, Vivien; Kern, Friederike (Second International Multimodal Communication Symposium (MMSYM) - Book of Abstract)
Keywords: gesture; explanations; conversation analysis

Perception and Consideration of the Explainees’ Needs for Satisfying Explanations - Schaffer, Michael Erol; Terfloth, Lutz; Schulte, Carsten; Buhl, Heike M.

Explainers’ Mental Representations of Explainees’ Needs in Everyday Explanations - Schaffer, Michael Erol; Terfloth, Lutz; Schulte, Carsten; Buhl, Heike M. (Joint Proceedings of the xAI-2024 Late-breaking Work, Demos and Doctoral Consortium. 3793)

The mental representation of the object of explanation in the process of co-constructive explanations - Schaffer, Michael; Buhl, Heike M. (53rd DGPs Congress - Abstracts)

Explaining the Technical Artifact Quarto!: How Gestures are used in Everyday Explanations - Lohmer, Vivien; Terfloth, Lutz; Kern, Friederike (First International Multimodal Communication Symposium - Book of Abstract)
Keywords: gesture; dual nature; explanations; architecture; relevance

Approaches of Assessing Understanding Using Video-Recall Data - Lazarov, Stefan Teodorov; Schaffer, Michael; Ronoh, Erick Kiprop

Erklärungsverläufe und -inhalte aus Sicht Erklärender - eine qualitative Studie - Schaffer, Michael; Buhl, Heike M.

Adding Why to What? Analyses of an Everyday Explanation - Terfloth, Lutz; Schaffer, Michael; Buhl, Heike M.; Schulte, Carsten
In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.