A Heuristic Proposal for Measuring AI Burnout

Abstract As the integration of generative artificial intelligence accelerates across professional and academic domains, emerging forms of cognitive fatigue and psychological friction remain uncaptured by traditional diagnostic frameworks. This preprint introduces a working hypothesis regarding AI Burnout and presents a novel, heuristic research scale designed to empirically test this phenomenon. While existing psychometric instruments measure general burnout, technostress, or system dependency, this scale incorporates two critical additions: exhaustion stemming from monitoring machine-generated output and a profound sense of alienation from one's own work. The proposed instrument is structured into four distinct dimensions—Burnout (Exhaustion, Detachment, Reduced Effectiveness), Attention and Motivation, Autonomy of Thought (Delegation, Need for Confirmation, Discomfort Without AI, Control Over Thinking, Work Alienation), and Real-World Impact. Outlining methodological guidelines for cross-sectional administration, longitudinal tracking (6, 12, and 24 months), factor validation, and falsification criteria, this work serves as an open foundational framework for researchers investigating the psychological and cognitive implications of human-AI interaction.