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Department of Informatics Human-centered Information Systems Engineering

Feedback in the Age of GenAI: Skin Conductance and Heart Rate Reactions to Feedback on AI-Assisted Work

Knowledge workers increasingly co-create their work outcomes with generative AI (GenAI), and those outcomes are routinely evaluated by colleagues, supervisors, or clients. Receiving feedback is a core part of knowledge work, and it typically evokes affective reactions that shape what people do next — how defensive they are, and how willing they are to revise their work.
Working with GenAI may change this dynamic. When GenAI takes over part of the task, workers invest less effort and may feel less personally invested in the result. If an outcome feels less tied to one’s own actions, evaluative feedback on it may simply matter less — producing weaker affective reactions and, potentially, a greater willingness to revise. This has direct implications for how iterative, feedback-driven collaboration works in GenAI-supported environments.
Affective reactions are not only subjective experiences — they show up in the body. This thesis measures them in two ways: electrodermal activity (EDA) / skin conductance and heart rate and heart rate variability (HR / HRV). Complementing self-reports with such physiological measures is a core methodological ambition of NeuroIS. The goal is to understand, how working with GenAI (vs. without) shapes workers’ physiological reactions to feedback on their work outcomes, and how these reactions relate to invested effort and to the extent of subsequent revision.

What You Will Do

  • Review  the literature across information systems / NeuroIS, human–AI collaboration, feedback and affective reactions, and the psychophysiology of EDA and HR/HRV.
  • Refine the study design.  Develop an experimental design for physiological measurement.
  • Set up and pilot data collection.  Prepare the experimental recording setup with clean event markers, so signals can be time-locked to each feedback event.
  • Run the experiment  with participants, following ethical and data-protection requirements (ethics review, informed consent, debriefing).
  • Process and analyze the signals.  Preprocess data using established tools and relate them to condition, effort, and revision behavior.
  • Write up  the findings, discussing contributions to human–AI collaboration research and implications for feedback culture in GenAI-supported work.

Your Profile

  • Genuine interest in empirical, experimental research on human–AI collaboration and the future of knowledge work
  • Willingness to work with data in Python or R
  • Interest in conducting lab experiments
  • Nice to have (not required):  prior exposure to signal processing, physiological/biometric data, or NeuroIS methods.

Please get in touch with a short email including your CV, a current transcript of records, a few sentences on why this topic interests you, and the planned start/finish date: hise@ifi.uzh.ch