This study offers a comparative, mixed methods investigation into how artificial intelligence (AI) can strengthen data informed decision making across three core administrative domains which include: resource allocation, student early warning systems, and teacher evaluation under contrasting policy regimes. By juxtaposing reform trajectories and governance practices in selected developed countries with Zambia’s policy context, the research evaluates technical performance, fairness trade offs, and institutional readiness. The empirical core comprises AI prototype development and fairness audits in Zambian partner institutions, complemented by comparative policy analysis and stakeholder interviews in developed country cases. The thesis integrates quantitative model evaluation (discrimination, calibration, lead time, and subgroup fairness), prescriptive optimisation scenarios for equitable resource distribution, and qualitative process tracing of governance, legitimacy, and capacity. Outcomes include evidence on where AI improves administrative outcomes, a tested governance playbook for responsible deployment in low resource settings, and policy recommendations that reconcile technical trade offs with normative choices about equity and accountability.
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- Maliro Ngoma (Autor:in), 2025, Comparative Education Policy Reform in Developed Countries and Zambia, München, GRIN Verlag, https://www.grin.com/document/1677200
