From Qualification Silos to Capability Pathways: Reconsidering the Australian Qualifications Framework in an Era of Artificial Intelligence, Lifelong Learning, and Deterministic Recognition Systems

This discussion paper examines structural tensions within the Australian tertiary education system arising from the separation of higher education and vocational education and training (VET) despite their shared foundation within the Australian Qualifications Framework (AQF). While the AQF provides a unified qualifications architecture, recognition of prior learning, credit transfer, and licensing pathways remain institutionally fragmented and frequently discretionary. The paper argues that historical distinctions between universities and VET providers are increasingly challenged by developments in artificial intelligence, advanced workplace technologies, lifelong learning expectations, and changing labour market requirements. Particular attention is given to the asymmetry between university-to-VET and VET-to-university recognition processes, using construction licensing pathways as a worked example. A forward-looking model is proposed based on deterministic competency mapping, auditable recognition systems, and capability-based progression rather than institution-based progression. The paper introduces the concept of a national capability-resolution framework capable of algorithmically mapping learning outcomes, qualifications, and licensing requirements while preserving regulatory oversight and competency verification. The paper contributes to policy discussions regarding educational mobility, workforce development, licensing reform, and the future integration of Australian tertiary education.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-18
DOI
https://doi.org/10.5281/zenodo.20743348
Primary Topic
Higher Education Learning Practices
Type
article
Field-Weighted Citation Impact
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article

From Qualification Silos to Capability Pathways: Reconsidering the Australian Qualifications Framework in an Era of Artificial Intelligence, Lifelong Learning, and Deterministic Recognition Systems

Gregory Adamson
Zenodo (CERN European Organization for Nuclear Research)
Higher Education Learning Practices
article

From Qualification Silos to Capability Pathways: Reconsidering the Australian Qualifications Framework in an Era of Artificial Intelligence, Lifelong Learning, and Deterministic Recognition Systems

Gregory Adamson
article en

Abstract

This discussion paper examines structural tensions within the Australian tertiary education system arising from the separation of higher education and vocational education and training (VET) despite their shared foundation within the Australian Qualifications Framework (AQF). While the AQF provides a unified qualifications architecture, recognition of prior learning, credit transfer, and licensing pathways remain institutionally fragmented and frequently discretionary. The paper argues that historical distinctions between universities and VET providers are increasingly challenged by developments in artificial intelligence, advanced workplace technologies, lifelong learning expectations, and changing labour market requirements. Particular attention is given to the asymmetry between university-to-VET and VET-to-university recognition processes, using construction licensing pathways as a worked example. A forward-looking model is proposed based on deterministic competency mapping, auditable recognition systems, and capability-based progression rather than institution-based progression. The paper introduces the concept of a national capability-resolution framework capable of algorithmically mapping learning outcomes, qualifications, and licensing requirements while preserving regulatory oversight and competency verification. The paper contributes to policy discussions regarding educational mobility, workforce development, licensing reform, and the future integration of Australian tertiary education.

Zenodo (CERN European Organization for Nuclear Research)
Decent work and economic growth
Openalex Percentile: Top 1%
Higher Education Learning Practices
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