Artificial Intelligence in Healthcare and Education
Abstract
Artificial intelligence is rapidly becoming embedded in everyday life through an expanding range of applications, services, and products. As its potential to improve diagnosis, personalize treatment, and enhance operational efficiency becomes increasingly evident, healthcare is undergoing profound transformation. However, trust and distrust operate as dual mechanisms shaping technology diffusion: trust facilitates adoption, whereas distrust constrains large-scale deployment. Trust therefore remains a persistent barrier to the widespread use of artificial intelligence in healthcare services. At present, empirical evidence on the pathways linking trust and acceptance of AI medical conversational agents (AIMCAs) remains limited. Grounded in trust theory, this study aimed to develop and validate a multidimensional trust-perception scale for AIMCAs, establish its dimensional structure and psychometric quality, and examine its associations with an external acceptance-related behavioral criterion. Methodologically, the study first used grounded-theory-informed abductive qualitative analysis to identify the structure of public trust perceptions of AIMCAs and generated and screened measurement items through expert Q-sorting; independent samples were then used for exploratory and confirmatory factor analyses, followed by assessments of internal consistency, test-retest reliability and absolute agreement, within-construct indicator convergence, discriminant validity, and criterion-related validity. Parallel analysis and the scree plot jointly supported a five-factor solution. Principal axis factoring with Direct Oblimin oblique rotation yielded a 15-item, five-dimensional structure, with the five common factors explaining 76.07% of the total variance. Confirmatory factor analysis further supported a five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust; model-fit indices, standardized factor loadings, latent-variable correlations, and residual diagnostics collectively provided evidence for its internal structure. The Fornell-Larcker criterion and bootstrap confidence intervals for HTMT jointly provided evidence for internal discriminant validity among the five AIMCA trust dimensions. An ordinal logit model using actual use frequency as an external behavioral criterion was statistically significant overall, likelihood-ratio χ²(5) = 71.963, p < 0.001, McFadden pseudo-R² = 0.125. Interactional trust showed the strongest association with higher use frequency (OR = 3.096, 95% CI [2.257, 4.246]). The resulting scale captures multidimensional public trust perceptions of AIMCAs and provides a structured measurement basis for research on acceptance-related behavior. The findings support a 15-item, five-dimensional structure comprising cognitive trust, affective trust, functional trust, human-like trust, and interactional trust. An ordinal logit model using self-reported AIMCA use frequency as an external behavioral criterion provided additional criterion-related evidence. The scale can be used to characterize multidimensional public trust perceptions of AIMCAs and provides a structured measurement foundation for subsequent research on acceptance, use intention, continuance intention, and actual use.
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