Public Orientations Toward Artificial Intelligence in a South Korean Online Quota Sample: A Mixed-Indicator Profile Analysis

Abstract

Public responses to artificial intelligence (AI) may combine capability, critical awareness, expected benefits, global outlook, and affect in configurations that are not captured by a single optimism–resistance continuum. We analyzed an unweighted, non-probability online quota sample of 2000 South Korean adults aged 18–69 collected in August 2025; the sample is not nationally representative. A mixed-indicator finite-mixture model combined three continuous indicators with categorical measures of global outlook and affective balance. The retained three-profile solution yielded model-estimated sample shares of 9.4% highly engaged enthusiasts, 61.1% mainstream optimists, and 29.5% cautious ambivalents. Prior-year generative AI use, treated as antecedent behavioral experience, was 88.1%, 74.8%, and 56.0%, respectively, and concurrent perceptions of AI presence and diffusion differed across profiles (all pooled Wald p < 0.001). Cautious ambivalents placed greater adjusted emphasis on a safe society and trustworthy government, whereas work–life balance was no longer significant after false discovery rate adjustment. Sensitivity analyses excluding the two-item critical-awareness indicator and restricting affect to its two common categories preserved the broad profile ordering (ARI = 0.942 and 0.915). Bootstrap stability was weaker for the smallest highly engaged profile, with 25 of 100 resamples yielding ARI < 0.50. The findings support sample-level heterogeneity in AI orientations while underscoring limits from non-probability sampling, brief measurement, classification instability, and cross-sectional self-report data.