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)

Numerical solution of nonlinear Schrödinger equation by a hybrid pseudospectral-variational quantum algorithm - Köcher, Nikolas; Rose, Hendrik; Bharadwaj, Sachin S.; Schumacher, Jörg; Schumacher, Stefan (Scientific Reports)
Abstract The time-dependent one-dimensional nonlinear Schrödinger equation (NLSE) is solved numerically by a hybrid pseudospectral-variational quantum algorithm that connects a pseudospectral step for the Hamiltonian term with a variational step for the nonlinear term. The Hamiltonian term is treated as an integrating factor by forward and backward Fourier transforms, which are here carried out classically. This split allows us to avoid higher-order time integration schemes, to apply a first-order explicit time stepping for the remaining nonlinear NLSE term in a variational algorithm block, and thus to avoid numerical instabilities. We demonstrate that the analytical solution is reproduced with a small root mean square error for a long time interval over which a nonlinear soliton propagates significantly forward in space while keeping its shape. We analyze the accuracy and complexity of the quantum algorithm, the expressibility of the ansatz circuit and compare it with classical approaches. Furthermore, we investigate the influence of algorithm parameters on the accuracy of the results, including the temporal step width and the depth of the quantum circuit.

Optically and remotely controlling localization of exciton-polariton condensates in a potential lattice - Ai, Qiang; Wingenbach, Jan; Yang, Xinmiao; Wei, Jing; Hatzopoulos, Zaharias; Savvidis, Pavlos G.; Schumacher, Stefan; Ma, Xuekai; Gao, Tingge (Physical Review Applied)

Nebentätigkeiten von Lehramtsstudierenden während des Langzeitpraktikums: Lerngelegenheiten oder Belastungen? - Beckmann, Timo; Caruso, Carina; Homann, Hanna (Lehrerbildung auf dem Prüfstand)
There is currently a shortage of teachers in schools, which means that an increasing number of people without full qualifications are being employed. In the form of part-time work, students also take on jobs at schools that differ from those that are part of a long-term school internship in the teacher education pro gramme, as the long-term internship aims to professionalize prospective teachers, while part-time work is intended to alleviate any shortages. One condition for long-term internships to contribute to the profes sionalization of prospective teachers is the support of students by mentors and, associated with this, the intentional design and reflection of learning opportunities during the internship, which is not associated with meeting the needs of schools. It can be concluded that students who work in schools in the form of a part-time job alongside their long-term internship are confronted with a wide variety of role expectations and tasks, which can have an influence on their experience of stress, among other things. Based on a survey of students (n = 134), this article examines the extent to which students combine their long-term internship with part-time work, what tasks they have during this part-time work at school, how they are supported and to what extent they experience stress.

SemDial 2025 – Bialogue. Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue

“I'm Actually More Interested in AI Than in Computer Science” - 12-Year-Olds Describing Their First Encounter with AI - Lenke, Michael; Lehner, Lukas; Landman, Martina (2025 IEEE Global Engineering Education Conference (EDUCON))

Enhancing AI Interaction through Co-Construction: A Multi-Faceted Workshop Framework - Lenke, Michael; Schulte, Carsten (2025 IEEE Global Engineering Education Conference (EDUCON))

Émilie Du Châtelet on Time - Hagengruber, Ruth Edith (The Routledge Companion to Philosophy of Time)

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))

Die (produktive) Rolle von generativer KI im erziehungswissenschaftlichen Diskurs - Herzig, Bardo (Erziehungswissenschaft. Mitteilungen der Deutschen Gesellschaft für Erziehungswissenschaft. Erziehungswissenschaft nach ChatGPT)

Higher Stakes, Healthier Trust? An Application-Grounded Approach to Assessing Healthy Trust in High-Stakes Human-AI Collaboration - Johnson, David S.
Human-AI collaboration is increasingly promoted to improve high-stakes decision-making, yet its benefits have not been fully realized. Application-grounded evaluations are needed to better evaluate methods for improving collaboration but often require domain experts, making studies costly and limiting their generalizability. Current evaluation methods are constrained by limited public datasets and reliance on proxy tasks. To address these challenges, we propose an application-grounded framework for large-scale, online evaluations of vision-based decision-making tasks. The framework introduces Blockies, a parametric approach for generating datasets of simulated diagnostic tasks, offering control over the traits and biases in the data used to train real-world models. These tasks are designed to be easy to learn but difficult to master, enabling participation by non-experts. The framework also incorporates storytelling and monetary incentives to manipulate perceived task stakes. An initial empirical study demonstrated that the high-stakes condition significantly reduced healthy distrust of AI, despite longer decision-making times. These findings underscore the importance of perceived stakes in fostering healthy distrust and demonstrate the framework's potential for scalable evaluation of high-stakes Human-AI collaboration.