Governing the Algorithmic Classroom: A Comparative Policy Analysis of National AI Strategies in Public Education Systems
DOI:
https://doi.org/10.37792/8s7zjb81Keywords:
Algorithmic Governance, Comparative Policy Analysis, Sociotechnical Imaginaries, Data Colonialism, Pedagogical Agency, Sustainable Public Innovation Ecosystems (SPIE)Abstract
There is a rapid adoption of artificial intelligence (AI) within the public education system, which represents a major shift in state governance, regulatory oversight, and public administration. The comparative policy analysis examines the structural, regulatory, and pedagogical aspects of national AI strategies across multiple geopolitical archetypes, including rights-based restrictive regimes (the European Union), decentralized market-driven systems (the United States), state-centric optimization regimes (China and Singapore), emerging democratic regimes (India), and sovereign digital development regimes (Bangladesh and Sub-Saharan Africa). Utilizing a hybrid comparative approach grounded in Most Different Systems Design (MDSD), this study systematically measures and indexes the “rhetoric-reality gap” in national policies and juxtaposes the prevailing technocratic Optimization Paradigm and the idealistic Restructuring Paradigm. The paper’s empirical evaluation of high-stakes algorithmic failures, including the 2020 Ofqual examination scandal in the UK, and its focus on the 2026 OECD Digital Education Outlook’s key findings on the “Performance-Learning Gap” should be taken as a wake-up call to the systemic loopholes of “metacognitive laziness” and corporate control capture that endanger public education. Finally, we operationalize the Sustainable Public Innovation Ecosystem (SPIE) model as a dynamic governance blueprint, providing cost, sequenced policy recommendations and a novel, non-algorithmic alternative assessment architecture intended to preserve pedagogical agency and democratic equity in the algorithmic age.
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