We are in the process of curating a list of this year’s publications — including links to social media, lab websites, and supplemental material. Currently, we have 68 full papers, 23 LBWs, three Journal papers, one alt.chi paper, two SIG, two Case Studies, one Interactivity, one Student Game Competition, and we lead three workshops. One paper received a best paper award and 13 papers received an honorable mention.
Disclaimer: This list is not complete yet; the DOIs might not be working yet.
Your publication from 2025 is missing? Please enter the details in this Google Forms and send us an email that you added a publication: contact@germanhci.de
A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media
Thomas Mildner (University of Bremen), Daniel Fidel (University of Bremen), Evropi Stefanidi (University of Bremen, TU Wien), Paweł W. Woźniak (TU Wien), Rainer Malaka (University of Bremen), Jasmin Niess (University of Oslo)
Abstract | Tags: Case Study, Dark Patterns, Full Paper | Links:
@inproceedings{Mildner2025ComparativeStudy,
title = {A Comparative Study of How People With and Without ADHD Recognise and Avoid Dark Patterns on Social Media},
author = {Thomas Mildner (University of Bremen), Daniel Fidel (University of Bremen), Evropi Stefanidi (University of Bremen, TU Wien), Paweł W. Woźniak (TU Wien), Rainer Malaka (University of Bremen), Jasmin Niess (University of Oslo)},
url = {dm.tzi.de, website
https://de.linkedin.com/company/dml-bremen, research group linkedin
https://www.linkedin.com/in/mildner-thomas/, author\'s linkedin},
doi = {10.1145/3706598.3713776},
year = {2025},
date = {2025-04-26},
urldate = {2025-04-26},
abstract = {Dark patterns are deceptive strategies that recent work in humancomputer interaction (HCI) has captured throughout digital domains, including social networking sites (SNSs). While research has identified difficulties among people to recognise dark patterns effectively, few studies consider vulnerable populations and their experience in this regard, including people with attention deficit hyperactivity disorder (ADHD), who may be especially susceptible to attention-grabbing tricks. Based on an interactive web study with 135 participants, we investigate SNS users’ ability to recognise and avoid dark patterns by comparing results from participants with and without ADHD. In line with prior work, we noticed overall low recognition of dark patterns with no significant differences between the two groups. Yet, ADHD individuals were able to avoid specific dark patterns more often. Through an interactive study, we expand previous work by understanding dark patterns in a realistic environment and offer insights into their effect on vulnerable populations.},
keywords = {Case Study, Dark Patterns, Full Paper},
pubstate = {published},
tppubtype = {inproceedings}
}
Evaluating an AI Documentation Assistant for Anesthesiology Teams
Stephan Huber (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Ronja Fricke (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Caroline Pütz (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Lennart Baumeister (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Christina Dilling (University Hospital Würzburg, Germany), Oliver Happel (University Hospital Würzburg, Germany), Simon Ottenhaus (KENBUN IT AG, Karlsruhe, Germany), Anja Nagel (KENBUN IT AG, Karlsruhe, Germany), Matthias Dunkelberg (KENBUN IT AG, Karlsruhe, Germany) Tobias Grundgeiger (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg)
Abstract | Tags: Case Study | Links:
@inproceedings{Huber2025EvaluatingAi,
title = {Evaluating an AI Documentation Assistant for Anesthesiology Teams},
author = {Stephan Huber (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Ronja Fricke (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Caroline Pütz (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Lennart Baumeister (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg, Germany), Christina Dilling (University Hospital Würzburg, Germany), Oliver Happel (University Hospital Würzburg, Germany), Simon Ottenhaus (KENBUN IT AG, Karlsruhe, Germany), Anja Nagel (KENBUN IT AG, Karlsruhe, Germany), Matthias Dunkelberg (KENBUN IT AG, Karlsruhe, Germany) Tobias Grundgeiger (Chair of Psychological Ergonomics, Julius-Maximilians-Universität Würzburg)},
url = {https://www.mcm.uni-wuerzburg.de/psyergo/, website
https://youtu.be/JsODMowIlS8, full video},
year = {2025},
date = {2025-04-26},
urldate = {2025-04-26},
abstract = {In addition to ensuring patient safety during anesthetic inductions, anesthesiologists must document clinical interventions and administer drugs. This is a time-consuming and low priority task, which harms the documentation quality of anesthetic protocols. In this case study, we demonstrate how speech-based artificial intelligence (AI) assistants that leverage closed-loop communication can increase documentation quality. An evaluation in 40 scenarios in a medical high-fidelity simulator indicated that the AI documentation assistant facilitated earlier data entry and increased documentation precision. However, despite the objective advantages for data quality and patient safety, anesthesiologists experienced a higher temporal demand with the system. With this study, we contribute qualitative insights of how the AI documentation assistant benefited anesthesiologists' work style and affected their interactions within the team. Future research should aim to design AI assistants that enforce communication clarity while considering their impact on team dynamics.},
keywords = {Case Study},
pubstate = {published},
tppubtype = {inproceedings}
}