Artificial Intelligence (AI) including Generative Artificial Intelligence (GenAI), is increasingly deployed across higher education institutions. However, implementations tend to be fragmented: data protection, predictive analytics, institutional leadership, quality management, resource optimization, and user interaction are typically owned by different units and optimized in isolation. This fragmentation erodes both trust and effectiveness. Recent literature indicates that these technologies can lower the operating costs of public higher education while raising concerns about privacy, equity, and uneven institutional capacity. This paper addresses the operational question that such evidence leaves open, proposing an integrated six-pillar socio-technical framework that binds the dimensions above into a single closed-loop governance pipeline for distance learning. The framework couples a formal mathematical privacy guarantee and interpretable prediction with a governance layer grounded in distributed leadership and total quality management, an optimization layer based on multi-criteria decision analysis, and a human-in-the-loop interface aligned with recognized software quality and accessibility standards. The framework is illustrated through a proof-of-concept evaluation on anonymized learning management records from the Hellenic Open University, in which early risk detection, staff acceptance, and intervention coverage all improve relative to a fragmented baseline. The paper concludes by discussing the conditions under which such gains are plausible and the limitations that full-scale validation must address.