Grammatik in der Schule. Eine Einführung - Becker, Tabea; Peschel, Corinna; Topalović, Elvira
Die Rolle der Grammatik im Deutschunterricht wird in der sprachdidaktischen Forschung intensiv diskutiert. Diese Einführung führt die Diskussion konstruktiv fort: Der Band bietet einen erweiterten, oft innovativen Blick auf grammatisches Lernen im Primar- und Sekundarbereich, er thematisiert neben den Grundlagen des Grammatikerwerbs auch Sprach(en)bewusstheit und Mehrsprachigkeit. Gemäß den aktuellen Bildungsstandards für das Fach Deutsch fokussiert er formale und funktionale Aspekte von Grammatik. Der Praxisteil liefert Methoden und Konzepte der Vermittlung als Best-Practice-Beispiele – nicht nur für Lehramtsstudierende, sondern auch für praktizierende Deutschlehrkräfte.
Keywords: Grammatik; Deutsch; Schule

Vincent Streichhahn (Hrsg.): Feministische Internationale. Texte zu Geschlecht, Klasse und Emanzipation 1832–1936 - Sandoval, Marisol (feministische studien)

NL2LS: LLM-based Automatic Linking of Knowledge Graphs - Ihtassine, Reda; Firmansyah, Asep Fajar; Srivastava, Nikit; Manzoor, Ali; Ngonga Ngomo, Axel-Cyrille; Sherif, Mohamed Ahmed (Proceedings of the Knowledge Capture Conference 2025)

Benchmarking Time Series Foundation Models for Short-Term Household Electricity Load Forecasting - Meyer, Marcel; Zapata Gonzalez, David Ricardo; Kaltenpoth, Sascha Benjamin; Müller, Oliver (IEEE Access)

A BFO-based ontological analysis of entities in Social XAI - Booshehri, Meisam; Buschmeier, Hendrik; Cimiano, Philipp (Proceedings of the 15th International Conference on Formal Ontology in Information Systems)
Since the emergence of the field of eXplainable Artificial Intelligence (XAI), a growing number of researchers have argued that XAI should consider insights from the social sciences in order to adapt explanations to the expectations and needs of human users. This has led to the emergence of a field called Social XAI, which is concerned with understanding how explanations are actively shaped in the interaction between a human user and an AI system. Recognizing this turn in XAI toward making XAI systems more “social” by providing explanations that focus on human information needs and incorporating insights from human–human explanatory interactions, in this paper we provide a formal foundation for Social XAI. We do so by proposing novel ontological accounts of the key terms used in Social XAI based on Basic Formal Ontology (BFO). Specifically, we provide novel ontological accounts for explanandum, explanans, understanding, explanation, explainer, explainee, and context. In doing so, we discuss multifaceted entities in Social XAI (having both continuant and occurrent facets; e.g., explanation) and the relationship between understanding and explanation. Additionally, we propose solutions to seemingly paradoxical views on some terms (e.g., social constructivist vs. individual constructivist perspective on explanandum).

GLIDE: Knowledge Graph Linking using Distance-Aware Embeddings - Becker, Alexander; Ngonga Ngomo, Axel-Cyrille; Sherif, Mohamed (The Semantic Web – ISWC 2025)
The number of datasets on the web of data increases continuously. However, the knowledge contained therein cannot be fully utilized without finding links between the entities contained in these datasets. Equivalent entities can not be identified solely by checking the equivalence of IRIs because of the different origins and naming schemes of different data providers. Yet, such equivalences can be discovered by computing the similarity of their attributes. In this paper we propose GLIDE, an approach that links entities from two different datasets by embedding a joint model of these datasets enriched by additional relations describing the similarity of literals. The joint model is embedded into a latent vector space while paying attention to juxtaposing similar literals. We evaluate our approach against state-of-the-art algorithms using real-world datasets commonly used in link discovery literature. The results show that GLIDE outperforms all baselines on 5 of 7 datasets with perfect or near-perfect accuracy. Our approach achieves its best performance on datasets that feature several literals with similarities. Our experiments indicate that researchers should not only pay attention to equal literals in knowledge graph embedding but should also be aware of the distance between similar literals.
Keywords: becker sherif enexa sailproject dice simba ngonga whale

Leveraging Cognitive States for Adaptive Scaffolding of Understanding in Explanatory Tasks in HRI - Groß, André; Richter, Birte; Thomzik, Bjarne; Wrede, Britta (arXiv:2503.19692)
Understanding how scaffolding strategies influence human understanding in human-robot interaction is important for developing effective assistive systems. This empirical study investigates linguistic scaffolding strategies based on negation as an important means that de-biases the user from potential errors but increases processing costs and hesitations as a means to ameliorate processing costs. In an adaptive strategy, the user state with respect to the current state of understanding and processing capacity was estimated via a scoring scheme based on task performance, prior scaffolding strategy, and current eye gaze behavior. In the study, the adaptive strategy of providing negations and hesitations was compared with a non-adaptive strategy of providing only affirmations. The adaptive scaffolding strategy was generated using the computational model SHIFT. Our findings indicate that using adaptive scaffolding strategies with SHIFT tends to (1) increased processing costs, as reflected in longer reaction times, but (2) improved task understanding, evidenced by a lower error rate of almost 23%. We assessed the efficiency of SHIFT's selected scaffolding strategies across different cognitive states, finding that in three out of five states, the error rate was lower compared to the baseline condition. We discuss how these results align with the assumptions of the SHIFT model and highlight areas for refinement. Moreover, we demonstrate how scaffolding strategies, such as negation and hesitation, contribute to more effective human-robot explanatory dialogues.

SHIFT: An Interdisciplinary Framework for Scaffolding Human Attention and Understanding in Explanatory Tasks - Groß, André; Richter, Birte; Wrede, Britta (arXiv:2503.16447)
In this work, we present a domain-independent approach for adaptive scaffolding in robotic explanation generation to guide tasks in human-robot interaction. We present a method for incorporating interdisciplinary research results into a computational model as a pre-configured scoring system implemented in a framework called SHIFT. This involves outlining a procedure for integrating concepts from disciplines outside traditional computer science into a robotics computational framework. Our approach allows us to model the human cognitive state into six observable states within the human partner model. To study the pre-configuration of the system, we implement a reinforcement learning approach on top of our model. This approach allows adaptation to individuals who deviate from the configuration of the scoring system. Therefore, in our proof-of-concept evaluation, the model's adaptability on four different user types shows that the models' adaptation performs better, i.e., recouped faster after exploration and has a higher accumulated reward with our pre-configured scoring system than without it. We discuss further strategies of speeding up the learning phase to enable a realistic adaptation behavior to real users. The system is accessible through docker and supports querying via ROS.

Multilingual Relation Extraction - A Survey - Manzoor, Ali; Speck, René; Zahera, Hamada Mohamed Abdelsamee; Saleem, Muhammad; Moussallem, Diego; Ngonga Ngomo, Axel-Cyrille (IEEE Access)

Assessing AI Literacy: A Systematic Review of Questionnaires with Emphasis on Affective, Behavioral, Cognitive, and Ethical Aspects - Lenke, Michael; Klowait, Nils; Biere, Lea; Schulte, Carsten (Lecture Notes in Computer Science)

The presentation of self in the age of ChatGPT - Klowait, Nils; Erofeeva, Maria (Frontiers in Sociology)
Contemporary debates about artificial intelligence (AI) still treat automation as a straightforward substitution of human labor by machines. Drawing on Goffman’s dramaturgical sociology, this paper reframes AI in the workplace as supplementary rather than substitutive automation. We argue that the central—but routinely overlooked—terrain of struggle is symbolic-interactional: workers continuously stage, conceal, and re-negotiate what counts as “real” work and professional competence. Large language models (LLMs) such as ChatGPT exemplify this dynamic. They quietly take over the invisible, routinised tasks that underpin cognitive occupations (editing, summarizing, first-draft production) while leaving humans to enact the highly visible or relational facets that sustain occupational prestige. Drawing on diverse sources to illustrate our theoretical argument, we show how individual workers, dramaturgical teams, and entire professional fields manage impressions of expertise in order to counter status threats, renegotiate fees, or obscure the extent of AI assistance. The paper itself, having been intentionally written with the ‘aid’ of all presently available frontier AI models, serves as a meta-reflexive performance of professional self-staging. The dramaturgical framework clarifies why utopian tales of friction-free augmentation and dystopian narratives of total displacement both misread how automation is actually unfolding. By foregrounding visibility, obfuscation, and impression management, the article presents a differentiated case for AI’s impact on the performative structure of work, outlines diagnostic tools for assessing real-world AI exposure beyond hype-driven headlines, and argues for a more human-centered basis for evaluating policy responses to the ‘fourth industrial revolution.’ In short, AI enters the labor process not as an autonomous actor, but as a prop within an ongoing social performance—one whose scripts, stages, and audiences remain irreducibly human.

Leveraging VR Tools for Inclusive Education: Implications from Sign Language Learning in VRChat - Erofeeva, Maria; Klowait, Nils; Belov, Mikael; Soulié, Yoann (Communications in Computer and Information Science)

Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks - Muschalik, Maximilian; Fumagalli, Fabian; Frazzetto, Paolo; Strotherm, Janine; Hermes, Luca; Sperduti, Alessandro; Hüllermeier, Eyke; Hammer, Barbara (The Thirteenth International Conference on Learning Representations (ICLR))

Explaining Outliers using Isolation Forest and Shapley Interactions - Visser, Roel; Fumagalli, Fabian; Hüllermeier, Eyke; Hammer, Barbara (Proceedings of the European Symposium on Artificial Neural Networks (ESANN))
Keywords: FF

Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory - Fumagalli, Fabian; Muschalik, Maximilian; Hüllermeier, Eyke; Hammer, Barbara; Herbinger, Julia (Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS))

A BFO-based ontology of context for Social XAI - Booshehri, Meisam; Buschmeier, Hendrik; Cimiano, Philipp (Abstracts of the 3rd TRR 318 Conference: Contextualizing Explanations)

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.

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection - Spliethöver, Maximilian; Knebler, Tim; Fumagalli, Fabian; Muschalik, Maximilian; Hammer, Barbara; Hüllermeier, Eyke; Wachsmuth, Henning (Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers))
Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task, language model, and context provided. Finding an effective prompt is, therefore, often a trial-and-error process. Most existing approaches to automatic prompting aim to optimize individual techniques instead of compositions of techniques and their dependence on the input. To fill this gap, we propose an adaptive prompting approach that predicts the optimal prompt composition ad-hoc for a given input. We apply our approach to social bias detection, a highly context-dependent task that requires semantic understanding. We evaluate it with three large language models on three datasets, comparing compositions to individual techniques and other baselines. The results underline the importance of finding an effective prompt composition. Our approach robustly ensures high detection performance, and is best in several settings. Moreover, first experiments on other tasks support its generalizability.

Protecting physics students from pseudoscience - combining strategies for a comprehensive teaching approach - Webersen, Yvonne; Riese, Josef (European Journal of Physics)
Abstract Given the presence of fake news and pseudoscience (often disguised as physics), the importance of inoculating citizens against this type of misinformation has gained increasing attention in education. This is a goal that all subjects should pursue equally from their respective disciplinary perspectives. The paper presents a teaching approach to protect physics students from misinformation and pseudoscience by combining these three common strategies: First, understanding the fundamental principles of the Nature of Science. Second, identifying techniques of Science Denial. And third, applying heuristics for evaluating (supposedly) scientific information. Finally, the paper offers practical suggestions for applying these strategies in a master's-level physics course, using examples from the field of physics.

Entanglement between dependent degrees of freedom: Quasiparticle correlations - Barkhausen, Franziska; Ares Santos, Laura; Schumacher, Stefan; Sperling, Jan (Physical Review A)