Predictive analytics platforms now guide many universities’ retention and advising strategies, yet their rapid adoption raises urgent equity questions. This paper examines three issues: (1) how education institutions currently deploy predictive models; (2) which ethical and practical risks like bias, privacy loss, and ‘at risk’ labeling emerge in real-world use; and (3) how such tools can be integrated with teaching practices to support students without amplifying inequality. We combine a theory-driven literature review drawing on Self-Determination Theory, the Community of Inquiry framework, the Technology Acceptance Model, and sociological perspectives with forthcoming empirical demonstrations on two open datasets. The study offers actionable guidance for researchers, developers, and decision-makers pursuing responsible learning analytics by mapping adoption patterns, surfacing risk mechanisms, and outlining equity-centred design principles.