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Journals / Sakarya University Journal of Education / 2026 / Volume: 16 Issue: 2

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

Mesut Aydemir(ANADOLU UNIVERSITY)0000-0002-2035-1292
Pages
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Publication Date
2026-08-10
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Implications for platform providers, content creators, and credential policymakers are discussed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Educational YouTube constitutes a globally accessible, practitioner-produced corpus of structured learning content.
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Key Findings & Data: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.
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Conclusion & Impact:Implications for platform providers, content creators, and credential policymakers are discussed.
🔬Design:Review & Systematic Survey
📍Scope:2015 Verileri
⚙️Variables:Microcredentials · Educational YouTube · AI-Assisted Learning Outcome Extraction

Abstract

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. 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. 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. 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. 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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Cite This Article

Aydemir, M. (2026). AI-Assisted Learning Outcome Extraction from Educational YouTube Content: A Conceptual Framework for Microcredential Issuance. Sakarya University Journal of Education, Volume: 16 Issue: 2. https://doi.org/10.19126/suje.1931440

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