The tension between personalized learning demands and standardized evaluation mechanisms presents a persistent challenge in contemporary education. This study proposes a comprehensive personalized education assessment framework driven by generative artificial intelligence technologies. The framework adopts a five-layer hierarchical architecture integrating data collection, processing, intelligent analysis, assessment generation, and feedback optimization components. ChatGLM3-6B, fine-tuned on 50,000 expert-curated programming feedback instances assembled through a human-in-the-loop process combining authentic instructor records, newly authored examples, and AI-assisted human-verified content, enables contextually responsive feedback generation, while dynamic learner profiling and knowledge graph modeling support precise diagnostic assessment. These findings suggest that generative AI can effectively operationalize personalized assessment at scale while maintaining pedagogical quality and transparency.
