["European Journal of Education, Volume 61, Issue 3, September 2026. ", "\nABSTRACT\nGenerative artificial intelligence (GAI) tools have outpaced instruments for assessing learners' readiness in programming education. This study developed and validated the GAI‐Assisted Programming Learning Readiness Scale (GAP‐LRS), integrating Social Cognitive Theory and Self‐Determination Theory. In a two‐phase design, Phase 1 (N = 350) used exploratory factor analysis and Phase 2 (N = 565) used confirmatory factor analysis and structural equation modelling; both samples were Taiwanese university programming students. The 28‐item, six‐factor structure—self‐efficacy, outcome expectations, environmental support, autonomy, competence and relatedness—showed good fit (CFI = 0.992, RMSEA = 0.019). The two higher‐order factors correlated at r = 0.17, indicating distinct but complementary facets. Competence and autonomy partially mediated environmental support's effect on self‐efficacy, whereas relatedness did not. Subject to forthcoming criterion validation, the GAP‐LRS provides a candidate framework for institutions and educators to identify which dimension of learner preparedness—volitional choice, experiential mastery or perceived institutional support—warrants targeted pedagogical or policy intervention.\n"]