The Generative Artificial Intelligence Literacy Scale (GAILS): Development, Validation, and Measurement Invariance Across Sex and Occupational Status Groups

Generative Artificial Intelligence (GenAI) tools are rapidly reshaping how people seek information, learn, and work, while also raising concerns about misinformation, bias, privacy, and academic integrity across both educational and workplace contexts. However, many existing AI literacy scales targeted broad AI knowledge rather than GenAI-specific competencies (e.g., prompt formulation, evaluating generated outputs). Emerging GenAI literacy measures have also focused primarily on university students and non-North American samples, limiting their generalizability. We developed and validated a GenAI literacy scale (GAILS) in a diverse North American adult sample (N = 341; Mage = 39.00 years; 38.42% students, 61.58% workforce). The GAILS showed excellent internal consistency (α = 0.973, 95% CI [0.968, 0.978]). Exploratory factor analysis supported a three-factor solution (KMO = 0.959; Bartlett’s χ²(561) = 5784.58, p < .001), and confirmatory factor analysis further supported the model (CFI = 0.967, TLI = 0.965, SRMR = 0.058, RMSEA = 0.087). Items were designed to assess three domains: (a) Adaptive Operational Skills, (b) Responsible GenAI Literacy, and (c) Critical Evaluation & Autonomous Use. Measurement invariance analyses supported scalar invariance across sex (female vs. male) and occupational status (student vs. workforce) groups. Construct validity was supported by positive correlations with GenAI acceptance (r = 0.81) and GenAI trust (r = 0.47). Semantic embedding analysis further supported the conceptual structure of the scale by showing distinct semantic clustering for Responsible GenAI Literacy and meaningful conceptual proximity between Adaptive Operational Skills and Critical Evaluation & Autonomous Use. These results suggest that the GAILS is a psychometrically and conceptually supported tool for assessing GenAI literacy gaps and informing educational, workplace, and policy interventions.