<![CDATA[The systematic integration of artificial intelligence into educational assessment has progressed through distinct developmental stages, each characterized by specific technological capabilities, pedagogical orientations, and methodological frameworks. This article examines the sequential stages of artificial intelligence application in the assessment methodology of independent learning, tracing the evolution from rule-based automation through machine learning classification, natural language processing, deep learning analytics, and generative artificial intelligence. Drawing on empirical evidence from recent systematic reviews and meta-analyses, the article identifies five principal stages of AI deployment in independent learning assessment: diagnostic readiness assessment, adaptive process monitoring, automated formative evaluation, summative competency certification, and reflective metacognitive calibration. Each stage is analyzed in terms of its technological infrastructure, psychometric validity, pedagogical affordances, and limitations. The findings indicate that the most effective assessment systems integrate multiple stages within a coherent developmental trajectory, progressively transferring regulatory responsibility from the AI system to the learner. The article concludes with recommendations for designing stage-appropriate AI assessment architectures that support learner autonomy while maintaining rigorous standards of educational measurement.]]>
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