Beyond Disposition: AI Knowledge Predicts Anthropomorphization of a Language Model Better Than Personality Traits in Lay and Expert Populations

要旨

Anthropomorphizing Artificial Intelligence (AI), i.e., ascribing human-like mind or emotions to it, is widespread but varies across individuals. We tested three proposed dispositional predictors of anthropomorphism (need for cognition, need for structure, loneliness) in a general population (N = 307) and an AI expert sample (N = 130). Using a vignette design based on excerpts from a dialogue between the large language model LaMDA and one of its engineers, we found that none of the three dispositional traits predicted anthropomorphism. Instead, higher levels of AI knowledge decreased anthropomorphism across both samples. Experts reported higher AI knowledge and lower anthropomorphism than laypersons. For laypersons, anthropomorphism increased intentions to use LaMDA. For experts it did not, but was correlated with discomfort. In both samples, anthropomorphism was associated with greater moral care, i.e., not switching off LaMDA against "its will." Our findings highlight the role of knowledge and expertise in perceptions of AI.

著者
Martina Mara
Johannes Kepler University Linz, Linz, Austria
Lara Bauer
Johannes Kepler University Linz, Linz, Austria
Marisa Victoria. Tschopp
scip AG, Zurich, Switzerland
Hannah Grosswieser
Johannes Kepler University, Linz, Austria
Johannes Kraus
University of Mainz, Mainz, Germany

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Relationships with AI

P1 - Room 130
7 件の発表
2026-04-13 20:15:00
2026-04-13 21:45:00