An introduction to hyper‑personalised blended learning (HPBL) for skills and assessments
Education systems across Southeast Asia face mounting pressure to deliver deeper learning, stronger skills alignment, and scalable personalisation. A significant portion of student learning time remains unstructured and underutilised, which limits the development of higher‑order thinking, self‑directed learning and industry‑relevant competencies.
This session introduces hyper‑personalised blended learning (HPBL) as a policy‑aligned pedagogical and system architecture designed to address these challenges. Powered by generative AI and implemented through a unified web‑based platform, HPBL was deployed over 12 weeks in two engineering courses at Universiti Teknologi MARA, Malaysia. The session presents evidence showing how an AI tutor within the HPBL environment can measurably improve system‑level indicators of learning maturity including prompt quality, cognitive depth and learner autonomy.
Learning analytics including engagement quality, cognitive demand, persona shifts and competency alignment will be showcased to demonstrate how HPBL provides actionable insights for educators, informs institutional decision‑making, and strengthens national skills ecosystems.
Designed for immediate classroom adoption, HPBL offers a practical approach to integrating AI into teaching and learning without increasing instructor workload. This model will be relevant for ministries, universities, and TVET systems seeking to build resilient and future‑ready learning environments.
Wednesday 23 September 16:00 - 16:30 Leadership Summit: HE & TVET
AI
Assessment
Digital strategy
Speakers
.png?ext=.png){kind=logo}
Associate Professor of Mechanical Engineering, Universiti Teknologi MARA (UiTM)
.png?ext=.png){kind=logo}
.png?ext=.png){kind=logo}
.png?ext=.png){kind=logo}
-4000px-(2).png?ext=.png){kind=logo}
