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    AIEJ

    AICE International Education Journal

    A peer-reviewed, open-access journal publishing empirical research on continuing professional development, the measurement of learning, and the application of artificial intelligence to professional education.

    Volume 1Issue 1May 2026Inaugural Issue

    From the Editors

    AIEJ exists to address a question that traditional CPD literature has rarely posed with rigour: does the credit a learner receives accurately reflect what they have actually learned?

    We publish work that applies measurement science, learning analytics, and algorithmic auditing to professional education — with an emphasis on reproducibility, open data, and methodological transparency.

    — The Editorial Board, AIEJ

    Current Issue · Vol. 1, No. 1

    Original Research

    Theoretical Review|Open Access|Peer Reviewed

    From Seat-Time to Competency: Re-Examining the Measurement Substrate of Continuing Professional Development

    AICE Research Group · AIEJ 2026; 1(1): 1–14 · DOI: 10.00000/aiej.2026.01.001

    Competency-based education (CBE) proposes that professional certification be anchored to demonstrable capability rather than to elapsed instructional time. Despite four decades of advocacy, the regulatory machinery of Continuing Professional Development (CPD) remains overwhelmingly time-based, denominated in hours, credits, or points. This paper reviews the conceptual basis of CBE, identifies the measurement gap that has impeded large-scale adoption, and presents the Effective Learning Words (ELW) framework as a candidate substrate for unifying time-based and competency-based regimes. We argue that ELW-anchored assessment can preserve the administrative convenience of hour-counting whilst introducing the criterion validity that CBE requires.

    Read full article →
    Original Article|Open Access|Peer Reviewed

    Quantifying Credit Allocation Discrepancies in Continuing Professional Development: A Comparative Audit of Ten Accredited CPD Courses Against an Algorithmic Effective-Learning-Words Standard

    AICE Research Group · AIEJ 2026; 1(1): 1–14 · DOI: 10.00000/aiej.2026.01.001

    A blinded comparative audit of ten randomly selected CPD-accredited courses found that nominal credit allocations diverged from algorithmically derived learning-content estimates by an average of 25–50%, with all ten courses over-credited relative to their measurable instructional substance.

    Read full article →

    Aims & Scope

    AIEJ welcomes original research, systematic reviews, methodological papers, and registered reports on the measurement, accreditation, and delivery of professional education. Areas of particular interest include credit-allocation auditing, AI-assisted assessment, learning analytics, and the equivalence of formal and self-directed learning.

    Editorial Standards

    • · Double-blind peer review
    • · Open access under CC BY 4.0
    • · Pre-registration encouraged
    • · Open data and reproducible code required where feasible
    • · Conflicts of interest disclosed in full

    AIEJ is published by AICE. The journal operates editorially independent of AICE's commercial activities. Disclosure: research authored by the AICE Research Group is reviewed by external editors with no affiliation to AICE.

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