DOI: 10.19126/suje.1931440 ISSN: 2146-7455

AI-Assisted Learning Outcome Extraction from Educational YouTube Content: A Conceptual Framework for Microcredential Issuance

Mesut Aydemir
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Educational YouTube constitutes a globally accessible, practitioner-produced corpus of structured learning content. Despite learners developing genuine and verifiable competencies through sustained engagement, informal video-based learning remains excluded from formal qualification frameworks, creating a systemic gap between acquired capability and recognized achievement. Purpose: This paper proposes the AI- Augmented Epistemic Design and Microcredential Derivation (AEDMD) framework, which reconceptualizes educational YouTube content as intentional epistemic design and illustrates how AI-assisted learning outcome extraction can translate that design into formally issued microcredentials. Methods: A conceptual synthesis methodology was employed, integrating literature across educational technology, artificial intelligence in education, epistemic design theory, and microcredential policy. A structured database search across ERIC, Scopus, and Web of Science (January 2015–December 2024) returned 634 candidate sources; after duplicate removal and title/abstract screening against explicit inclusion and exclusion criteria, 124 sources were reviewed in full, and 42 were selected for substantive engagement. Proposed Framework: The AEDMD framework comprises five epistemically grounded stages: (1) multi-modal AI content analysis; (2) learning outcome extraction and epistemic profiling; (3) epistemic level classification using a Bloom-SOLO synthesis; (4) workload calibration and ECTS credit assignment; and (5) open badge microcredential issuance with portfolio integration. A hypothetical illustrative application to a data science YouTube channel conceptually illustrates the framework’s potential applicability. Contribution: AEDMD advances epistemic design theory as an analytical lens for informal digital learning, provides a theoretically grounded AI pipeline for extracting learning outcomes, and proposes a potentially scalable microcredential issuance pathway for open and distance learning institutions and higher education providers. Implications for platform providers, content creators, and credential policymakers are discussed.

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