most citedDrag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs

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cs.HC20261 cited

Drag or Traction: Understanding How Designers Appropriate Friction in AI Ideation Outputs

A. Baki Kocaballi, Joseph Kizana, Sharon Stein +1

Seamless AI presents output as a finished, polished product that users consume rather than shape. This risks design fixation: users anchor on AI suggestions rather than generating…

cs.HC20261 cited

"I'm happy even though it's not real": GenAI Photo Editing as a Remembering Experience

Yufeng Wu, Qing Li, Elise van den Hoven +1

Generative Artificial Intelligence (GenAI) is increasingly integrated into photo applications on personal devices, making editing photographs easier than ever while potentially inf…

cs.HC2026

Recommendation-as-Experience: A framework for context-sensitive adaptation in conversational recommender systems

Raj Mahmud, Shlomo Berkovsky, Mukesh Prasad +1

While Conversational Recommender Systems (CRS) have matured technically, they frequently lack principled methods for encoding latent experiential aims as adaptive state variables.…

cs.HC2025

"She was useful, but a bit too optimistic": Augmenting Design with Interactive Virtual Personas

Paluck Deep, Monica Bharadhidasan, A. Baki Kocaballi

Personas have been widely used to understand and communicate user needs in human-centred design. Despite their utility, they may fail to meet the demands of iterative workflows due…

cs.HC2025

Unplug, Mute, Avoid: Investigating smart speaker users' privacy protection behaviours in Saudi Homes

Abdulrhman Alorini, Yufeng Wu, Abdullah Bin Sawad +2

Smart speakers are increasingly integrated into domestic life worldwide, yet their privacy risks remain underexplored in non-Western cultural contexts. This study investigates how…

cs.HC2025

Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits

Raj Mahmud, Shlomo Berkovsky, Mukesh Prasad +1

Conversational Recommender Systems (CRSs) deliver personalised recommendations through multi-turn natural language dialogue and increasingly support both task-oriented and explorat…