This study examines the integration of artificial intelligence (AI)–driven decision support tools within finance and operations research (OR) pedagogy, addressing the persistent gap between analytical theory and applied decision-making skills in higher education. The research investigates whether embedding AI-enabled tools into coursework improves learning outcomes, analytical reasoning, and decision quality. A quasi-experimental design was employed across four graduate cohorts (n = 212), comparing traditional instruction with AI-augmented pedagogy using machine-learning-based forecasting, optimization solvers, and interactive dashboards. Learning performance, decision accuracy, and cognitive engagement were measured using standardized assessments and project-based evaluations. Results indicate that students exposed to AI-driven tools achieved higher mean decision accuracy scores (↑18.6%), improved model-interpretation proficiency (↑22.4%), and reduced solution time in optimization tasks (↓27.1%) relative to control groups. Regression analysis shows AI tool usage to be a statistically significant predictor of learning performance (β = 0.41, p < 0.01). The findings suggest that structured integration of AI decision support systems enhances experiential learning and aligns finance and OR education with contemporary industry practices, supporting a shift toward technology-embedded analytical pedagogy.