3 papers
cs.HC2026
From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
Moussa Kassem Sbeyti, Joshua Holstein, Philipp Spitzer +2
High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted annotation has therefore becom…
cs.HC2026
Believing vs. Achieving -- The Disconnect between Efficacy Beliefs and Collaborative Outcomes
Philipp Spitzer, Joshua Holstein
As artificial intelligence (AI) becomes increasingly integrated into workflows, humans must decide when to rely on AI advice. These decisions depend on general efficacy beliefs, i.…
cs.HC2025
From Consumption to Collaboration: Measuring Interaction Patterns to Augment Human Cognition in Open-Ended Tasks
Joshua Holstein, Moritz Diener, Philipp Spitzer
The rise of Generative AI, and Large Language Models (LLMs) in particular, is fundamentally changing cognitive processes in knowledge work, raising critical questions about their i…