@article{66650, author = {{Sagolla, Nils and Seifert, Andreas and Bohndick, Carla and Buhl, Heike M.}}, journal = {{Journal for Educational Research Online}}, number = {{1}}, pages = {{31--48}}, publisher = {{Waxmann}}, title = {{{Skills Help, but They Don’t Help All at Once: Changes in the Relationship Between Study- Relevant Skills and Student Stress as Well as Study Satisfaction During the First Two Years of Teacher Education}}}, doi = {{10.31244/jero.2026.01.02}}, volume = {{18}}, year = {{2026}}, } @inbook{66753, author = {{Kaemper, Mara and Buhl, Heike M. and Klingsieck, Katrin B.}}, booktitle = {{Prokrastination: Erkennen, Verstehen, Reduzieren}}, editor = {{Klingsieck, Katrin B. and Koppenborg, Markus}}, isbn = {{9783662728994}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Verhaltensbezogene Prävention von Prokrastination: Das PROFI-Training}}}, doi = {{10.1007/978-3-662-72900-7_10}}, year = {{2026}}, } @article{66671, author = {{Gladow, Viviane and Buhl, Heike M.}}, journal = {{Frontiers in Education}}, title = {{{Partner models of explainers: a qualitative interview study}}}, doi = {{10.3389/feduc.2026.1891741}}, volume = {{11}}, year = {{2026}}, } @article{66690, author = {{Scharlau, Ingrid}}, journal = {{Forschung und Lehre}}, number = {{8}}, pages = {{22--23}}, publisher = {{Deutscher Hochschulverband}}, title = {{{Krise oder Rekonfiguration?}}}, volume = {{33}}, year = {{2026}}, } @book{66749, editor = {{Klingsieck, Katrin B. and Koppenborg, Markus}}, isbn = {{9783662728994}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Prokrastination: Erkennen, Verstehen, Reduzieren}}}, doi = {{10.1007/978-3-662-72900-7}}, year = {{2026}}, } @inbook{59754, author = {{Scharlau, Ingrid and Seifert, Andreas}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien}}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-1}}, pages = {{339--365}}, publisher = {{Springer Nature}}, title = {{{Empirische Methoden der psychologiedidaktischen Forschung}}}, year = {{2025}}, } @inbook{59752, author = {{Scharlau, Ingrid and Patrzek, Justine and Schreiber, Christine}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien}}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-0}}, pages = {{89--118}}, publisher = {{Springer Nature}}, title = {{{Psychologiedidaktik durch Analyse von Kommunikation}}}, year = {{2025}}, } @inbook{59753, author = {{Scharlau, Ingrid and Christine, Schreiber}}, booktitle = {{Psychologiedidaktik an allgemeinbildenden und beruflichen Schulen: Ein Lehrbuch mit Unterrichtsmaterialien}}, editor = {{Scharlau, Ingrid and Bender, Elena and Patrzek, Justine and Schreiber, Christine}}, isbn = {{978-3-662-69480-1}}, pages = {{271--300}}, publisher = {{Springer Nature}}, title = {{{Schreiben im Psychologieunterricht unterstützen}}}, year = {{2025}}, } @unpublished{59839, abstract = {{In many scientific approaches, especially in those that try to foster explainability of Artificial Intelligences, a narrow conception of explaining prevails. This narrow conception implies that explaining is a one-directional action in which knowledge is transferred from the explainer to an addressee. By studying the amount of agency in metaphors for explaining in scientific texts, we want to find out – or at least to contribute a partial answer to the question – why this narrow conception is so dominant. For our analysis, we use a linguistic conception of agency, transitivity. This concept allows to specify the degree of agency or effectiveness of the action in a verbalised event. It is defined by several component parts. We detail and discuss both the parameters of and global transitivity. Overall, transitivity of explaining metaphors has a rather common pattern across metaphors. Agency is not high and reduced in characteristic aspects: The metaphors imply that the object of explaining is static, i.e., is not changed within the explanation, and that explaining is the activity of one person only. This pattern may account for the narrow conception of explaining. It contrasts strongly with current co-constructive or sociotechnical approaches to explainability.}}, author = {{Scharlau, Ingrid and Rohlfing, Katharina J.}}, publisher = {{Center for Open Science}}, title = {{{Agency in metaphors of explaining: An analysis of scientific texts}}}, year = {{2025}}, } @article{59756, abstract = {{A current concern in the field of Artificial Intelligence (AI) is to ensure the trustworthiness of AI systems. The development of explainability methods is one prominent way to address this, which has often resulted in the assumption that the use of explainability will lead to an increase in the trust of users and wider society. However, the dynamics between explainability and trust are not well established and empirical investigations of their relation remain mixed or inconclusive. In this paper we provide a detailed description of the concepts of user trust and distrust in AI and their relation to appropriate reliance. For that we draw from the fields of machine learning, human–computer interaction, and the social sciences. Based on these insights, we have created a focused study of empirical literature of existing empirical studies that investigate the effects of AI systems and XAI methods on user (dis)trust, in order to substantiate our conceptualization of trust, distrust, and reliance. With respect to our conceptual understanding we identify gaps in existing empirical work. With clarifying the concepts and summarizing the empirical studies, we aim to provide researchers, who examine user trust in AI, with an improved starting point for developing user studies to measure and evaluate the user’s attitude towards and reliance on AI systems.}}, author = {{Visser, Roel and Peters, Tobias Martin and Scharlau, Ingrid and Hammer, Barbara}}, issn = {{1389-0417}}, journal = {{Cognitive Systems Research}}, keywords = {{XAI, Appropriate trust, Distrust, Reliance, Human-centric evaluation, Trustworthy AI}}, publisher = {{Elsevier BV}}, title = {{{Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation}}}, doi = {{10.1016/j.cogsys.2025.101357}}, year = {{2025}}, } @misc{59922, author = {{Porwol, Philip and Scharlau, Ingrid}}, publisher = {{OSF}}, title = {{{An annotated corpus of elicited metaphors of explaining and understanding using MIPVU}}}, doi = {{10.17605/OSF.IO/Y6SMX}}, year = {{2025}}, } @article{59755, abstract = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine, trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception, for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated classification as advice. Research to improve such interactions often aims to foster the user’s trust, which in turn should improve the combined human-AI performance. Given that AI models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is an equally interesting target of research. We created two image classification scenarios in which the participants received mock-up AI advice. The quality of the advice decreases for a phase of the experiment. We studied the task performance, trust and distrust of the participants, and tested whether an instruction to remain skeptical and review each piece of advice led to a better performance compared to a neutral condition. Our results indicate that this instruction does not improve but rather worsens the participants’ performance. Repeated single-item self-report of trust and distrust shows an increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether the problems of under- and over-reliance have been mitigated. We discuss implications of our results for the usage of disclaimers before interacting with AI, as prominently used in current LLM-based chatbots, and for trust and distrust research.}}, author = {{Peters, Tobias Martin and Scharlau, Ingrid}}, journal = {{Frontiers in Psychology}}, keywords = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}}, title = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}}, doi = {{10.3389/fpsyg.2025.1574809}}, volume = {{16}}, year = {{2025}}, } @article{60144, author = {{Depenbusch, Sarah}}, journal = {{Frontiers in Computer Science}}, number = {{1553441}}, title = {{{VR-based avatar videos as an effective tool for process training in the context of digitalization?}}}, doi = {{10.3389/fcomp.2025.1553441}}, volume = {{7}}, year = {{2025}}, } @misc{59921, author = {{Scharlau, Ingrid and Miriam, Körber}}, publisher = {{OSF}}, title = {{{Metaphors in 24 WIRED Level 5 Videos (Data corpus)}}}, doi = {{10.17605/OSF.IO/94A2J}}, year = {{2025}}, } @techreport{61332, author = {{Buhl, Heike M. and Fisher, Josephine Beryl and Rohlfing, Katharina J.}}, publisher = {{OSF}}, title = {{{Role Perception Questionnaire: Co-construction. Scales manual}}}, year = {{2025}}, } @techreport{61433, author = {{Buhl, Heike M. and Herrmann, Paula and Bolinger, Dean X.}}, publisher = {{OSF}}, title = {{{TRR 318, Project A01, WP 2.1. Scales manual}}}, year = {{2025}}, } @techreport{61434, author = {{Buhl, Heike M. and Herrmann, Paula and Bolinger, Dean X.}}, publisher = {{OSF}}, title = {{{TRR 318, Project A01, WP 2.2. Scales manual}}}, year = {{2025}}, } @article{61244, abstract = {{Explanations play a crucial role in knowledge transfer and meaning-making and are often described as a co-constructive process in which multiple agents collaboratively shape understanding. However, the metaphors used to conceptualize explaining may influence how this process is framed. This study investigates the extent to which the co-constructive nature of explaining is represented in explaining metaphors. Using a systematic analysis of agency, we examined how these metaphors depict the explanation process and the roles of the agents involved. We found that explaining metaphors lack collaboration between explainer and addressee, constructiveness of the process, as well as bidirectionality and iterativeness. In light of current research on metaphorical framing, the study thus highlights the risk that such explaining metaphors may reinforce a non-co-constructive perspective on explaining and a top-down approach in the development of AI systems as well as other areas.}}, author = {{Porwol, Philip Fabian and Scharlau, Ingrid}}, journal = {{Frontiers in Psychology}}, title = {{{Is explaining more like showing or more like building? Agency in metaphors of explaining}}}, doi = {{https://doi.org/10.3389/fpsyg.2025.1628706}}, year = {{2025}}, } @inproceedings{62060, author = {{Bobe, Julia and Decker, Claudia and Klingsieck, Katrin B.}}, location = {{Lübeck}}, title = {{{Gestärkt durchs Studium - Universitäre Unterstützungsangebote zum Stress- und Selbstmanagement}}}, year = {{2025}}, } @inproceedings{62058, author = {{Bobe, Julia and Klingsieck, Katrin B.}}, location = {{Utrecht}}, title = {{{The Role of Subjective Discomfort in Procrastination - Feeling bad about unnecessary delaying and therefore, preventing it?}}}, year = {{2025}}, }