This study aims to: quantitatively analyze the implementation status of OBE frameworks across seven academic disciplines; evaluate current patterns of AI integration in curriculum design; identify gaps between theoretical potential and practical application; and propose a validated integrative model for AI-enhanced OBE curriculum design.

Methods: A mixed-methods approach was employed, combining systematic literature review (47 primary studies, 2023- 2026), bibliometric analysis (1,247 articles from Scopus/Wos), comparative analysis across seven disciplines using 12 quantitative indicators, and content analysis of educational policy documents from 15 countries. Statistical analyses included descriptive statistics, ANOVA, Pearson correlation, and multiple regression.

Results: Key findings reveal: average OBE implementation rate of 57% across all disciplines, with professional programs (medicine 82%, cybersecurity 79%) significantly outperforming humanities (32%) (F(6,40) = 18.7, p < 0.001); 73% of students use AI tools weekly, yet 68% of institutions lack clear AI integration policies; (3) a 28-percentage point gap between outcome clarity (80%) and outcome achievement (56%); (4) 81% of faculty report insufficient AI training; the proposed AI-OBE Model demonstrates 37% higher effectiveness when integrated within a clear pedagogical framework.

Conclusions: The transition toward AI-enhanced OBE curricula is an educational imperative, but success requires integrated pedagogical, technical, and ethical dimensions. The validated AI-OBE Model provides a comprehensive framework with quantitative benchmarks for implementation across diverse disciplinary contexts.