The construction and validation of the AI mindset scale (AIMS)

Abstract

As the dawn of artificial intelligence (AI) reshapes our future, a better understanding of the individual beliefs and attitudes toward AI becomes pivotal in harnessing its full potential. Therefore, this study aimed to develop and validate the AI Mindset Scale (AIMS), assessing the belief that AI usage enhances one's abilities and skills. The German sample ( N = 921; 58% female; M age = 30.90; SD age = 8.71 years), was randomly split into two subsamples for EFA ( n = 368) and CFA ( n = 553). EFA resulted in a two-factor solution with four items per factor. CFA supported the model fit of the hierarchical model, including an AIMS total score and the subscales growth and non-deskilling (CFI = .982; TLI = .973; RMSEA = .072; SRMR = .043), showing good reliability (total score, α = .82; ω = .91; growth, α = .91; ω = .92; non-deskilling, α = .91; ω = .92). The nomological network analysis revealed that the AIMS captures distinct facets, with growth primarily predicted by AI acceptance and openness, and non-deskilling primarily by AI fear and locus of control. • The AIMS measures the belief of the enhancing or deskilling effect of AI use. • The instrument comprises two subscales: Growth and Non-deskilling. • The 8-item scale shows high reliability and a clean latent structure. • AI fear, acceptance, and openness predict the AIMS total score. • The economic scale is applicable in psychology and human-AI interaction.

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