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Personalized experiences within single-user applications have become ubiquitous, yet when multiple people share a physical space, smart environments are either made to adapt to individual user preferences or to stereotypical predefined groups. Hence, mechanisms and design principles are needed to blend and share individual preferences. In this paper, we propose a multi-user music playback scenario and prototype a decentralized application that combines Solid Pods to store users’ music and sharing preferences, and a Mixed Reality application that enables users to blend individual preferences into a collective music experience using smart speakers. We take this exploratory scenario to suggest relevant design dimensions to consider when creating solutions for shared multi-user personalized experiences in smart environments.

Jannis Strecker-Bischoff, Kimberly Garcia, Sven Durrer, and Simon Mayer. 2026. From Me to We: Shared Multi-User Personalized Experiences in Smart Environments. In Companion of the 2026 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp Companion ’26), October 11–15, 2026, Shanghai, China. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3798063.3837234

Change blindness, the failure to detect substantial changes, is a known phenomenon that demonstrates one of the limits of human visual perception. Generative image models can now synthesize and edit photorealistic scenes on demand, with prompt-level control over the magnitude, semantic category and salience of a change while observers struggle to distinguish their output from real photographs. The availability of such fluid image adaptations brings fascinating possibilities when combined with change blindness, with respect to dataset sizes and a broadening of the range of testable changes. We present BlinkArt, a system which swaps AI-generated peripheral modifications into displayed images precisely during natural blinks. The system can autonomously author changes, producing self-documented visual narratives without hand-crafted prompts. BlinkArt’s approach can serve both as a controlled research paradigm and a gaze-reactive art installation in which the artwork evolves precisely where the viewer is not looking.

Dominik Steinmann, Jannis Strecker-Bischoff, Kenan Bektaş, Jessica Laraine Williams, and Simon Mayer. 2026. BlinkArt: Using Change Blindness For AI-Generated Image Modifications in Artworks. In Companion of the 2026 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp Companion ’26), October 11–15, 2026, Shanghai, China. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3798063.3837172

Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy's legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person's most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.

Meihe Xu, Jannis Strecker-Bischoff, Clément Guitton, Kenan Bektaş, Aurelia Tamò-Larrieux, and Simon Mayer. 2026. Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload. Behaviour & Information Technology (2026). https://doi.org/10.1080/0144929X.2026.2692098

@article{Xu2026LegallyCompliant,
author = {Xu, Meihe and Strecker-Bischoff, Jannis and Guitton, Cl\'{e}ment and Bekta\c{s}, Kenan and Tam\`{o}-Larrieux, Aurelia and Mayer, Simon},
title = {Legally compliant personalised prioritisation of privacy policy information shows no effect on user engagement, comprehension, or workload},
journal = {Behaviour \& Information Technology},
year = {2026},
publisher = {Taylor \& Francis},
doi = {10.1080/0144929X.2026.2692098},
url = {https://doi.org/10.1080/0144929X.2026.2692098},
abstract = {Privacy policies function as both legal documents and information sources for users, but their length and complexity often discourage engagement. In this paper, we investigate whether a personalised approach can address this issue by prioritising information that concerns individual users most while maintaining a policy's legal compliance on disclosure. We first explored whether personal characteristics can be used to predict a person's most concerned category and, hence, serve as a baseline for personalisation. We then conducted an eye-tracking experiment and interviews (n = 30) to understand the effectiveness of personalised reordering of privacy policies. In the interviews, many participants perceived personalised reordering as helpful, although others raised concerns about the invasion of privacy through this personalisation. The eye-tracking results indicate that personalised reordering leads to higher engagement for the first few sentences of a privacy policy. Based on our findings, we present design recommendations for creating legally compliant forms of privacy disclosures that encourage user engagement as well as discussions and implications on privacy disclosure compliance.},
keywords = {personalisation, privacy policies, personalised law, eye tracking, privacy disclosure, user study}
}