AI Driven Personalised Learning for Sustainable Higher Education in the Pacific

Personalised learning (PL) is increasingly promoted
as a strategy to enhance engagement and achievement in higher
education. Advances in adaptive systems, artificial intelligence, and
learning analytics enable tailored pathways and real-time feedback.
Meta-analyses report medium-to-large effects for intelligent
tutoring systems (ITS, g ≈ 0.6–0.7) and moderate gains for selfregulated
learning (SRL) interventions (d ≈ 0.69), while adaptive
platforms typically yield small-to-moderate improvements. Largescale
implementations, however, often produce only modest results
with considerable variance across contexts. This paper presents
a PRISMA-guided scoping review (2012–2025, n = 92 studies)
synthesising global evidence on PL modalities and applying
insights to Pacific higher education, where challenges of access,
equity, and cultural fit are acute. The contribution is twofold: first,
it explicitly links global effect-size evidence to equity outcomes
in low-resource contexts; second, it proposes an offline-first,
edge-based AI architecture tailored to Pacific higher education
to mitigate connectivity, privacy, and sustainability barriers.
The paper concludes with a context-sensitive research agenda
emphasising hybrid models, equity-first design, and governance
frameworks for sustainable implementation.