Navigating the Algorithmic Turn: A Dynamic Governance Framework for Ethical and Equitable AI Integration in Education
DOI:
https://doi.org/10.65655/5qccbx88Keywords:
Artificial Intelligence in Education, AI Ethics, Educational Policy, Governance Frameworks, Socio-Technical Systems, Digital Transformation, Equity in EducationAbstract
Artificial Intelligence (AI) is rapidly recasting educational practice, yet most institutions still govern its use through ad‑hoc or reactive measures. The resulting policy vacuum threatens academic integrity, equity, and public trust. This study develops and empirically validates the Dynamic AI Governance in Education (DAIGE) framework—a four‑pillar model designed to guide proactive, ethical, and inclusive AI adoption in both K‑12 and higher‑education settings.
Methods: A convergent‑triangulation mixed‑methods design combined (i) an NLP‑enhanced systematic review and global policy scan of 512 documents, (ii) qualitative comparative analysis of twelve early‑adopter institutions across four continents, and (iii) a three‑round Delphi study with 35 international experts. Latent‑topic modelling, cross‑case synthesis, and consensus statistics (IQR, ΔMedian) ensured analytic rigour.
Findings: Five high‑salience policy themes emerged—academic integrity, data privacy, teacher capacity, equity, and AI literacy—yet fewer than 10 % of institutions reported formal generative‑AI guidelines. Case‑study sites that adopted multi‑stakeholder co‑creation and tiered‑permission protocols achieved a 21‑percentage‑point higher teacher and student buy‑in (χ² = 9.67, p < .01) and halved academic‑misconduct incidents within one term. The Delphi panel reached consensus on 26 of 28 DAIGE elements, rating “Iterative Governance & Continuous Improvement” and “Inclusive Stakeholder Co‑Creation” as the most critical pillars.
Conclusions. DAIGE operationalises socio‑technical, diffusion, complexity, and stakeholder theory into a practical roadmap that can close the gap between lofty ethical principles and everyday classroom realities.
Implications. Institutional leaders, regulators, and EdTech developers can use DAIGE’s indicators as accreditation benchmarks, audit tools, and product‑alignment guides, ensuring that AI innovation advances educational quality and social justice in tandem
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