This research investigates the Longitudinal Impact of AI-Driven Adaptive Learning Systems on student retention and skill mastery across diverse socioeconomic and demographic groups. The study aims to empirically validate the claim that AI-based personalized instruction can enhance academic outcomes and ensure equitable learning opportunities compared to traditional online education models. Utilizing a multi-institutional, multi-cohort quasi-experimental design over five academic years, data were collected from 12 higher education institutions representing varying geographic regions and economic contexts. The sample included 18,000 students from undergraduate and vocational programs, divided into treatment and control groups. Variables included academic performance, course completion, skill mastery assessments, and post-graduation outcomes. Data were disaggregated by race, income, and location to examine equity dimensions. Statistical analyses, including mixed-effects regression models and longitudinal growth curve modeling, were applied to evaluate learning trajectories and retention over time.
Preliminary findings suggest that AI-driven adaptive systems significantly improve both retention and measurable skill mastery, particularly among students from lower-income backgrounds and underrepresented groups. However, disparities persist in institutions with limited digital infrastructure. The research concludes that while AI-adaptive learning platforms demonstrate strong potential to reduce achievement gaps, their impact on equity is contingent upon systemic access to technology and institutional readiness. The study provides critical evidence to guide policymakers, educators, and technologists in developing inclusive AI education ecosystems that promote long-term academic and career success. Across 12 institutions (n = 18,000), course completion in AI‑adaptive sections was 88.3% compared to 76.5% in matched traditional online sections (p < .001), which is the central finding of this study.
