Human Media

This studio explores rapid prototyping of shape-morphing fabrics as tangible interfaces by combining parametric modeling tools with accessible 3D printing techniques. Participants will experiment with two textile-based fabrication methods: (1) 3D printing on pre-stretched textiles for programmable morphing, and (2) printing TPU-based modular mesh structures with varying parameters to explore material properties. Through hands-on exercises, participants will investigate how material parameters—such as pattern, scale, thickness, and density—define fundamental rules for shape morphing and enable the creation of responsive, expressive, and multi-stable tangible artifacts. Emphasizing thinking-through-design for desired material behaviors, the studio invites HCI researchers and designers to collaboratively explore and critically reflect on the benefits, limitations, and future integration of shape-morphing interfaces.

We present an end-to-end workflow for privacyenhancing synthetic dataset generation and privacy-risk auditing by fine-tuning and prompting the open source Llama-3.1-8B with differential-privacy (DP) techniques. Two corpora are produced according to our workflow: a Raw-Generated Dataset without protection and a DP-Semantic Dataset created by adding DP noise controlled by a privacy budget. Using Anonymeter, aligned with EU Article 29 WP guidelines, we quantify singlingout, linkability, and inference risk. Experiments show high reidentification risk in the raw corpus, whereas the DP corpus, under a moderate budget, drastically lowers all three metrics while preserving data utility. Our reproducible, open framework offers a regulation-compliant recipe for safe data synthesis, augmentation, and city-scale applications of large language models.

Companion chatbots offer a potential solution to the growing epidemic of loneliness, but their impact on users' psychosocial well-being remains poorly understood, raising critical ethical questions about their deployment and design. This study presents a large-scale survey (n = 404) of regular users of companion chatbots, investigating the relationship between chatbot usage and loneliness. We develop a model explaining approximately 50% of variance in loneliness; while usage does not directly predict loneliness, we identify factors including neuroticism, social network size, and problematic use. Through cluster analysis and mixed-methods thematic analysis combining manual coding with automated theme extraction, we identify seven distinct user profiles demonstrating that companion chatbots can either enhance or potentially harm psychological well-being depending on user characteristics. Different usage patterns can lead to markedly different outcomes, with some users experiencing enhanced social confidence while others risk further isolation. These findings have significant implications for responsible AI development, suggesting that one-size-fits-all approaches to AI companionship may be ethically problematic. Our work contributes to the ongoing dialogue about the role of AI in social and emotional support, offering insights for developing more targeted and ethical approaches to AI companionship that complement rather than replace human connections.

BackgroundTheoretical models on cognitive performance predict that performance breaks down at higher task load levels, because of limiting cognitive resources. Experimental validation of these models is scarce, and they often don’t accommodate effects of invested effort and/or stressors. We experimentally measured a performance-curve as a function of increasing task load using a Tetris game. In addition, we investigated if performance breakdown occurs at different task load levels in the presence of a stressor. We hypothesized that an external stressor would shift the performance-curve to the left (i.e. lower task load levels) while maintaining the same form.MethodsTwenty-one participants completed one training day and two test days (data collection: 2023). During training, an individual break-off level on the Tetris game was determined with a staircase method. Subsequently, on the two test days participants played nine predetermined task load levels on and around their break-off level. On one test day they were exposed to heat load, and on the other test day to room temperatures. Task load levels and test room temperature were counterbalanced.ResultsThe results show that a sigmoid model is successful in fitting the performance decline pattern at high task load levels. Heat load did not seem to affect the parameters of the sigmoid curves.ConclusionsThis study provides experimental evidence for a performance-curve as a function of increasing task load, resembling the right side of the classic inverted U-curve. Contrary to our expectation, we did not find evidence for a stressor-induced shift of the individual performance-curve, presumably because the increase in body core temperature (TC) was insufficient to affect cognitive performance. Tetris seems a suitable tool to reproduce the performance-curve, and to perform research into factors that may affect the relation between mental effort and performance.

Perspective-Aware AI (PAi) is a computational innovation in human-AI interaction that allows users to view and interact through each other's perspectives by creating personalized computational models called chronicles. Chronicles capture cognitive and