Laura Schütz
Laura Schütz is a PhD candidate in Computer Science at Technical University of Munich. She also holds an MS in Design from Stanford. Her research focuses on modeling human perception and cognition for adaptive user interfaces.
Laura is an incoming Postdoc Fellow at the ETH AI Center, where she will focus on learning user mental models from multimodal interaction traces during human-AI interaction. She has previously organized multiple interdisciplinary workshops, e.g., at ISMAR, Harvard Kennedy School, and Stanford Graduate School of Business.
Affiliation: Technical University of Munich · incoming Postdoc Fellow, ETH AI Center.
Typing Behavior in Human-LLM Interaction: Keystroke Dynamics Reveal Cognitive Effort During Prompting
Laura Schütz, Yousri Cherif, Clara Sayffaerth, Thomas Weber, Francesco Chiossi
Proceedings of the ACM on Human-Computer Interaction
As Large Language Models (LLMs) become increasingly integrated into daily routines, understanding how users interact with these systems is crucial for effective human-AI collaboration. This work investigates keystroke dynamics as a behavioral measure of user mental effort and perceived output usefulness in human-LLM interaction. We conducted a user study (N = 36) to examine how task difficulty (easy vs. hard) and device type (desktop vs. mobile) influence typing behavior and workload (NASA-TLX) during interactions. Our results indicate that hard tasks led to significantly more keystrokes, slower typing, increased pauses, and higher self-reported workload. Device type had weaker effects, with mobile use slightly reducing input length and typing speed. While keystrokes captured differences in cognitive effort, they did not predict perceived LLM output usefulness. These findings highlight the potential of keystroke dynamics as real-time indicators of cognitive effort during LLM prompting, while also showing their limitations in capturing perceived collaboration success.
