Two Roads to Competence? How Cognitive and Affective Individual Differences Reach Applied Performance by Different Routes

Individual differences predict applied competence, but the routes by which they do so are rarely modelled. Drawing on investment and aptitude theory for cognitive resources and on appraisal theory for affective orientations, we propose an appraisal-correspondence principle: the closer an individual-difference variable stands to the outcome in its level of self-appraisal, the more directly it should act; the further away it is, the more mediation should be required. We tested the principle on pre-service teachers’ competence to teach with generative artificial intelligence (AI-TPACK), using a latent-variable model on cross-sectional data from 164 pre-service primary-school teachers in Türkiye. AI readiness (cognitive) and AI competence anxiety (affective) served as antecedents and generative AI literacy and AI self-efficacy as mediators. Fit was good, χ2(139) = 181.44, CFI = 0.978, TLI = 0.973, RMSEA = 0.045, and SRMR = 0.061, with 45.8% of AI-TPACK variance explained. Readiness produced a strong total effect (β = 0.600, 95% CI [0.444, 0.738]), half of it indirect; model comparison indicated that generative AI literacy, rather than self-efficacy, carries the mediated share, although the literacy → AI-TPACK path itself was only partially supported. Competence anxiety predicted neither mediator, and its direct path to AI-TPACK was small (β = −0.105, CI [−0.266, 0.059]) and not significant in any of 16 pre-specified data-handling variants. The cognitive road is robust; the affective road, as predicted by the principle, bypasses the mediators but leaves only a faint and uncertain trace. Full scalar invariance held across year of study and use frequency. We state the principle as a hypothesis for performance-based tests rather than as an established mechanism.