Focuses on the application of large language models in computer science education, investigating automated programming assessment, intelligent feedback generation, and learning diagnostic methods. It explores execution-evidence-based transparent assessment frameworks that extend code evaluation from binary scoring to multi-dimensional cognitive diagnosis, providing interpretable and auditable technical solutions for formative assessment in large-enrollment programming courses.