Sarcouncil Journal of Multidisciplinary

Abstract: The digital transformation of education has positioned personalization algorithms as influential gatekeepers that mediate access to knowledge. These computational systems, mainly adapted to commercially recommended architecture, now control the triggering of material exposure, sequence determination, difficulty calibration, speed regulation, and educational platforms. While personalization provides sufficient benefits in learning efficiency, retention, reduction in cognitive weight, and motivation, important concerns about equity and educational opportunity are revealed. Gatekeeping forms Learning Bubbles, where students face narrow courses that correspond to perceived abilities rather than development capacity. The phenomenon manifests through feedback loops, overfitting for early performance, reduces serendipitous search, and reinforces inequalities that affect the marginalized population. Case studies across mathematics and language learning platforms demonstrate how initial performance assessments trigger persistent gatekeeping mechanisms that fundamentally alter learning trajectories. Encouragingly, interventions including curriculum diversity guarantees, strategic randomness, transparent recommendation explanations, and equity-centered evaluation metrics have demonstrated potential to preserve personalization benefits while mitigating educational inequities. The findings highlight the critical importance of intentional design and continuous evaluation of algorithmic personalization to ensure these systems expand rather than contract educational opportunity.