AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education

Collaborative group projects are integral to computer science education, as they foster teamwork, problem-solving skills, and industry-relevant competencies. However, assessing individual contributions in group settings has long been challenging. Traditional assessment strategies, such as the equal distribution of grades or subjective peer assessments, often fall short in terms of fairness, objectivity, and scalability, particularly in large classrooms. This paper introduces a semi-automated, AI-assisted grading system that evaluates both project quality and individual effort using repository mining, communication analytics, and machine learning models. The system comprises modules for project evaluation, contribution analysis, and grade computation, and integrates seamlessly with platforms such as GitHub. A pilot deployment in a senior-level course demonstrated high alignment with instructor assessments, increased student satisfaction, and reduced instructor grading effort. We conclude by discussing implementation considerations, ethical implications, and proposed enhancements to broaden applicability.