Journal of Applied Learning and Teaching

Modelling optimal learning pathways: A Markov Decision Process approach to the pedagogy–heutagogy continuum

2026-03-11T19:03:24+11:00

This paper presents a novel application of an AI model based on a Markov Decision Process (MDP) and leverages Dynamic Programming and value iteration to model the student learning journey across pedagogical, andragogical, and heutagogical learning paradigms. In contrast to a traditional static educational model, the proposed approach can adapt to changing cohort engagement and progress to provide more personalised and effective learning. Thus, the model can provide deeper insights into how students navigate self-directed learning, stay motivated, and achieve job readiness by considering different learning states, choices, and their associated outcomes. This work offers a theoretical contribution by formalising the pedagogy-heutagogy continuum and a practical framework through integration of analytics systems to optimise the learning process. It establishes a conceptual shift where personalisation moves from a design choice to a mathematically optimised strategy, bridging educational theory with computational decision science. While the current model uses illustrative data, it establishes a scalable foundation for future empirical integration using learning analytics.

Book Review of Sabbaghan, Soroush (Ed., 2025). Navigating Generative AI in Higher Education: Ethical, Theoretical and Practical Perspectives. Edward Elgar Publishing.

2026-03-04T12:48:57+11:00

Is AI the solution to the problems that make higher education “ill” in the first place? Towards a technology-agnostic, future-proof approach

2026-03-04T10:06:46+11:00

Artificial intelligence (AI) is being deployed in higher education at an unprecedented scale and pace as a silver bullet for the “sick” higher education system. This opinion paper examines whether and to what extent AI is the solution to the problems that make higher education purportedly outmoded and/or dysfunctional in the first place. It begins by identifying two persistent challenges confronting the sector: uneven quality and enduring inequities in access. The discussion then turns to the deeper structural causes of these challenges, before examining four potential roles that AI is commonly assumed to play in addressing them. In doing so, the paper critically explores whether AI can meaningfully remedy higher education’s underlying problems, and concludes by proposing a technology-agnostic, future-proof approach to transforming higher education in more sustainable and principled ways. The position of this paper is that the future of higher education should not hinge on any particular technology, no matter how cutting-edge it may appear. Accordingly, a fundamental principle for the selection and use of technology in higher education is proposed, whereby a technology should be adopted only when it enables educators to do what they otherwise cannot, or when it demonstrably performs better than educators at an affordable cost, or when it performs as well as educators while reducing costs. Higher education should remain open to technological advancement, but it must not allow itself to be defined by it.

Book Review of Max S. Bennett (2024). A Brief History of Intelligence: Why the Evolution of the Brain Holds the Key to the Future of AI. William Collins.

2026-03-03T15:07:37+11:00

Book Review of S. Popenici, J. Rudolph, F. Ismail, & S. Tan (Eds., 2026). The Handbook of Artificial Intelligence in Higher Education. Edward Elgar.

2026-02-12T13:56:06+11:00

Book Review of Jasper Roe (2025). How to use Generative AI in educational research. Cambridge Elements. Research Methods in Education.

2026-02-11T18:30:27+11:00