<![CDATA[Artificial intelligence-supported learning management systems (AI-LMS) offer opportunities to prepare future teachers, yet evidence linking competence development to teaching readiness remains limited. This quantitative study combined a one-group pretest–posttest design with post-intervention partial least squares structural equation modeling (PLS-SEM). A census of 59 second-semester pre-service primary teachers from two accessible classes (30 and 29 participants) completed an eight-week Moodle AI-supported learning sequence. All 59 completed paired digital competence and teaching readiness questionnaires and post-intervention AI-LMS perception data. The instruments initially contained 40 Likert-type items; we retained 20 indicators for the PLS-SEM measurement model. We used Shapiro–Wilk tests, Wilcoxon signed-rank tests, effect sizes, and normalized gain to describe changes; we examined reliability, validity, bootstrapped paths, and indirect effects with PLS-SEM. Digital competence increased from 30.58 to 40.83 and teaching readiness from 55.31 to 74.32 (both p < .001). AI-LMS perceptions were associated with digital competence (β = .755) and teaching readiness (β = .408); digital competence was associated with readiness (β = .400) and statistically mediated the AI-LMS–readiness relationship (indirect β = .302, p = .001). The integrated findings identify digital competence as a potential bridging capacity, while the uncontrolled design and measurement-model limitations preclude causal conclusions.]]>
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