AI Empathy as a Human Disposition in Generative AI Interaction: Development and Preliminary Psychometric Evaluation of a Cognitive–Affective–Motivational Scale

Abstract

Generative artificial intelligence (GenAI) is increasingly embedded in human reasoning and problem solving, yet individuals differ in how they interpret AI behavior, respond affectively to its limitations, and regulate subsequent interaction. This study developed and preliminarily evaluated the 42-item AI Empathy Scale for Generative AI Interaction (AIES-G) as a measure of these human dispositions. The provisional 3 × 2 framework crosses cognitive, affective, and motivational domains with other- and self-oriented responses, yielding six subfactors: Perspective Taking, Imaginative Self-Projection, Empathic Concern, Personal Distress, Prosocial Intention, and Reflective Inquiry. Participants were 201 individuals from four educational cohorts who completed the AIES-G before and after GenAI-supported activities. Exploratory factor analysis of the 42 post-test items indicated adequate factorability (KMO = 0.859; Bartlett’s χ2(861) = 4225.91, p < 0.001). Parallel analysis and Velicer’s MAP criterion supported six factors (48.2% of item variance), but one factor mainly reflected negatively keyed items, and Prosocial Intention formed no distinct factor. Internal consistency was acceptable to strong across the six subscales and total scale. Scores excluding Personal Distress increased from pre-test to post-test (dz = 1.46), possibly reflecting partial overlap with instructed activities. Increases were larger for Perspective Taking, Imaginative Self-Projection, Prosocial Intention, and Reflective Inquiry than for Empathic Concern, whereas Personal Distress met the prespecified equivalence criterion. Findings provide preliminary psychometric evidence for a proposed multidimensional human disposition relevant to behavioral research on human–AI interaction; confirmatory, behavioral, and cross-context validation remains necessary.