<![CDATA[The rapid diffusion of artificial intelligence (AI) into schooling has begun to reshape how student learning is measured, yet the managerial pathways through which principals translate this technology into legitimate assessment practice remain poorly understood, particularly outside higher-education settings. This article examines the management strategies principals employ when implementing AI in learning assessment at the school level. Using a qualitative descriptive multi-site case study design, data were gathered through semi-structured interviews, classroom and meeting observations, and documentation review across six schools purposively selected for varying stages of AI adoption. Data were triangulated by source, method, and time, then analyzed using the Miles and Huberman interactive model of condensation, display, and conclusion drawing. Findings show that principals operationalize AI-assessment implementation through four interrelated managerial functions planning, organizing, actuating, and controlling supported by a cross-cutting emphasis on capacity building and stakeholder communication. The study contributes a context-grounded, function-based model that integrates classical school management theory with contemporary AI-governance concerns such as data ethics, algorithmic bias, and teacher agency, offering both theoretical refinement and practical guidance for principals navigating AI-based assessment reform.]]>
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