DOI:
https://doi.org/10.63385/ipt.v1i3.114Keywords:
Artificial Intelligence, Assessment Literacy, Knowledge Management, Knowledge Transfer, Teacher TrainingAbstract
As Artificial Intelligence (AI) reshapes educational assessment practices, there is a growing need to examine existing frameworks through the lens of Knowledge Management (KM). While models such as TALiP, Assessment for Learning (AfL), Popham’s Model, and Stiggins’ Five Pillars offer important foundations for assessment literacy, they lack structured mechanisms for systematic knowledge generation, transfer, and alignment with AI-generated insights. This study introduces a novel contribution: a seven-phase KM-AI integration framework designed to support responsible and pedagogically aligned AI adoption in educational assessment. Unlike existing approaches, this framework embeds KM principles into the full lifecycle of AI-supported assessment capturing tacit expertise, contextualizing algorithmic outputs, and enabling iterative learning across instructional settings. The framework is grounded in theoretical analysis and refined through a hypothetical use case and a documented institutional deployment of an AI-powered teaching assistant. Together, these cases illustrate how the framework can enhance teacher agency, support ethical AI use, and improve assessment coherence even in low-resource environments. The outcome is a practical roadmap for educators, policymakers, and developers that ensures AI tools strengthen rather than displace human-centered assessment practices. This study advances both theory and practice by providing an actionable, scalable model for KM-AI alignment in the era of digital transformation.
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