Abstract
Background/Objectives: Artificial intelligence (AI) is increasingly reshaping healthcare by influencing professional roles, learning demands, and career development. Guided by Self-Determination Theory and Social Cognitive Career Theory, this study examined the indirect association between attitudes toward AI and intrinsic motivation through career engagement among healthcare workers while accounting for demographic and occupational characteristics. Methods: A cross-sectional online survey was conducted among 902 healthcare workers in Türkiye. Ordinal confirmatory factor analyses based on polychoric correlations were estimated using diagonally weighted least squares. Alternative measurement models and one theoretically justified wording-method association were evaluated. Covariate-adjusted regression mediation analyses were performed using HC3 robust standard errors and 5000 nonparametric bootstrap resamples with bias-corrected confidence intervals. Common method variance, effect sizes, predictive relevance, subgroup analyses among nurses and midwives, and a sensitivity analysis using a 20-item intrinsic motivation scale were also examined. Results: The selected measurement models demonstrated acceptable to excellent fit, although the GAAIS S-1 model showed borderline RMSEA and two reverse-worded IMSE items retained weak loadings. A statistically significant indirect association between attitudes toward AI and intrinsic motivation was observed through career engagement (indirect association = 0.099, 95% bias-corrected bootstrap CI [0.071, 0.132]), accounting for 27.9% of the total association. The 20-item sensitivity analysis yielded a similar indirect association (indirect association = 0.105, 95% bias-corrected bootstrap CI [0.075, 0.139]). Profession-stratified analyses showed significant indirect associations in both occupational strata, although the AI-attitude–career-engagement association was stronger among nurses/midwives. Conclusions: More favorable attitudes toward AI were associated with greater career engagement and intrinsic motivation. Career engagement explained a meaningful, although not dominant, proportion of this association. Because the data were cross-sectional and self-reported, the findings should be interpreted as associative rather than causal. Supporting career engagement may be a plausible target for future intervention research aimed at facilitating healthcare workers’ adaptation to AI-enabled environments, although adaptation itself was not directly assessed in this study.