Help, Don’t Control: Measuring User Emotional Background in AI Assistant Interaction

Abstract

As AI assistants shift from answering questions to executing actions, small interaction choices can influence users’ moment-to-moment experience. We report an exploratory lab study (N=20) measuring users’ Emotional Background (EB) during three local device-maintenance tasks in a prompt-based assistant and a menu-based baseline interface. We derived an EEG-based EB index and triangulated it with eye tracking and post-task questionnaires. EB trajectories were task-dependent: the prompt-based assistant produced higher EB for straightforward app deletion, while the baseline yielded higher EB for malware checking and troubleshooting. EB decreases appeared to cluster around three interaction moments: formulating an initial request, encountering negatively framed system-health terminology, and processing rapid or ambiguous automation of consequential actions. Survey responses indicated greater willingness to delegate non-personal tasks than personal communications. We discuss these patterns as preliminary evidence and outline design considerations for pacing, framing, transparency, and prompt scaffolding.