Tootfinder

Beyond the Hype: Mapping Uncertainty and Gratification in AI Assistant Use
Karen Joy, Tawfiq Ammari, Alyssa Sheehan
https://arxiv.org/abs/2506.09220 https:…

Beyond the Hype: Mapping Uncertainty and Gratification in AI Assistant Use
This paper examines the gap between the promises and real-world performance of emerging AI personal assistants. Drawing on interviews with early adopters of devices like Rabbit R1 and Humane AI Pin, as well as services like Ohai and Docus, we map user experiences through the lens of Uses and Gratifications and Uncertainty Reduction Theory. We identify three core types of user uncertainty, functional, interactional, and social, and explore how each disrupts different user gratifications. We show…

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
Matthew Russell, Aman Shah, Giles Blaney, Judith Amores, Mary Czerwinski, Robert J. K. Jacob
https://arxiv.org/abs/2506.04167

Neural and Cognitive Impacts of AI: The Influence of Task Subjectivity on Human-LLM Collaboration
AI-based interactive assistants are advancing human-augmenting technology, yet their effects on users' mental and physiological states remain under-explored. We address this gap by analyzing how Copilot for Microsoft Word, a LLM-based assistant, impacts users. Using tasks ranging from objective (SAT reading comprehension) to subjective (personal reflection), and with measurements including fNIRS, Empatica E4, NASA-TLX, and questionnaires, we measure Copilot's effects on users. We also evaluate …