Adaptive Learning Platforms (ALPs) offer the potential to personalise instruction at scale while strengthening digital capability and cyber-resilience. Yet many existing systems optimise short-term performance rather than inclusivity, transparency, or educator readiness. This paper introduces a multistage framework that unifies inclusive pedagogy, AI-driven adaptivity, and embedded cyber-resilience across primary, secondary, tertiary, and postgraduate contexts. Synthesising research published between 2000 and 2025, the study derives stage-aligned design requirements, adaptive decision rules, and governance mechanisms for ethical and equitable personalisation. The framework formalises an actor–critic control loop with policy-based sequencing, fairness regularisation, and risk-aware gating for safety-critical learning, supported by privacy-preserving analytics. It further specifies educator-in-the-loop controls, professional capability tiers, and institutional dashboards that link adaptive decisions to classroom practice. The contributions are threefold: (1) a unified foundation connecting adaptive algorithms with inclusive and cyber-aware pedagogy, (2) a stage-wise mapping of learner challenges, signals, and intervention primitives, and (3) an implementation model embedding privacy, equity, transparency, and sustainability safeguards within adaptive learning ecosystems. The framework provides a conceptual pathway toward verifiable, inclusive, and human-centred ALPs that can scale responsibly within Education 5.0 environments.
Nowrozy et al. (Thu,) studied this question.