Abstract:

As artificial intelligence (AI) becomes structurally embedded across higher education and professional practice, institutions are under increasing pressure to deliver AI learning that is rapid, scalable, and responsive. Yet prevailing responses have largely treated AI education as a content-delivery problem, privileging tool familiarity over developmental progression. This paper argues that the central challenge is not content scarcity, but the absence of learner-sensitive educational architecture.

Drawing on internationally recognised human-centred and digital capability frameworks, the study proposes a differentiated, curation-based model for AI learning structured across two intersecting dimensions: learner context (students, educators, teachers, and professional services/administrative staff) and prior knowledge (beginner and advanced). Rather than generating new instructional materials, the approach curates and sequences high-quality existing resources into staged pathways aligned with ethical responsibility, cognitive readiness, and professional accountability.

The model reframes AI capability as developmental rather than additive. Beginner pathways provide scaffolded induction into conceptual understanding, ethical boundaries, and responsible experimentation tailored to students, professional staff, and administrative personnel. Advanced pathways introduce ambiguity, governance considerations, and evaluative judgment relevant to educators, professional practitioners, and institutional staff operating within academic and organisational contexts. In doing so, the study demonstrates how AI education can move beyond short-term technical proficiency toward sustained judgment formation across diverse learner groups.

The findings suggest that differentiated pathway design offers a scalable and sustainable alternative to one-size-fits-all AI training. By repositioning educators and professional services staff as architects of learning progression rather than producers of transient content, the model contributes a structurally grounded approach to cultivating AI judgment in an era of accelerating technological uncertainty.

Keywords:

Artificial Intelligence Education, Differentiated Learning, Digital Capability Frameworks, AI Literacy.