@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}},
}

@article{58109,
  abstract     = {{The present study aims to understand how metaphors are used in explanations. According to many current theories, metaphors have a conceptual function for the understanding of abstract objects. From this theoretical assumption, we derived the hypothesis that the lower the expertise of the addressee of an explanation, the more metaphors should be used. We tested this hypothesis on a relatively natural data set of 24 published videos with close to 100,000 words overall in which experts explain abstract, mostly scientific concepts to persons of different expertise, varying from minimal (children) to profound (expert). Contrary to our expectations, the frequency of metaphors did not decrease with expertise, but actually increased. This increase could be statistically substantiated with higher differences in expertise. The study contributes to a better understanding of the use of metaphors in actual explanatory processes and how metaphor use depends on contextual factors. It thus supports the expansion of the conceptual and linguistic perspective on metaphors to include the aspect of how metaphors are used by speakers.}},
  author       = {{Scharlau, Ingrid and Körber, Miriam and Sengupta, Meghdut and Wachsmuth, Henning}},
  journal      = {{Frontiers in Language Sciences}},
  keywords     = {{metaphor, conceptual metaphor, conceptual metaphor theory, metaphor usage, explaining, explanation}},
  pages        = {{1474924}},
  title        = {{{When to use a metaphor: Metaphors in dialogical explanations with addressees of different expertise}}},
  volume       = {{3}},
  year         = {{2024}},
}

@article{48543,
  abstract     = {{Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts—measured as breaks in the finger movement data before the correct gesture was carried out—when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.}},
  author       = {{Groß, A. and Singh, Amit and Banh, Ngoc Chi and Richter, B. and Scharlau, Ingrid and Rohlfing, Katharina J. and Wrede, B.}},
  journal      = {{Frontiers in Robotics and AI}},
  keywords     = {{HRI, XAI, negation, understanding, explaining, touch interaction, gesture}},
  title        = {{{Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue}}},
  doi          = {{10.3389/frobt.2023.1236184}},
  volume       = {{10}},
  year         = {{2023}},
}