Users entangle their lived experiences with AI predictions in menstrual tracking apps, leading to self-fulfilling prophecies, limited critical awareness from UI, and isolation for non-normative users.
Proceedings of the CHI Conference on Human Factors in Computing Systems , pages =
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4roles
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Mixed-Initiative Context reconceptualizes interaction context as a dynamic, jointly manageable structure that humans and AI can actively organize according to task needs.
MUIAnno is an expert-annotated dataset of mobile UI screens from iOS apps with structured JSON labels and baseline results for UI element detection.
Standard LLM chats produce high perceived understanding but low objective learning in students, while future-self explanations best align confidence with actual gains and guided hints maximize learning with moderate workload.
citing papers explorer
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"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps
Users entangle their lived experiences with AI predictions in menstrual tracking apps, leading to self-fulfilling prophecies, limited critical awareness from UI, and isolation for non-normative users.
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Mixed-Initiative Context: Structuring and Managing Context for Human-AI Collaboration
Mixed-Initiative Context reconceptualizes interaction context as a dynamic, jointly manageable structure that humans and AI can actively organize according to task needs.
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MUIAnno: An Expert-Annotated Dataset and Evaluation Benchmark for Mobile UI Understanding
MUIAnno is an expert-annotated dataset of mobile UI screens from iOS apps with structured JSON labels and baseline results for UI element detection.
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Confidence Without Competence in AI-Assisted Knowledge Work
Standard LLM chats produce high perceived understanding but low objective learning in students, while future-self explanations best align confidence with actual gains and guided hints maximize learning with moderate workload.