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

Songmei Yu, Andrew Zagula

Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings is a long-standing challenge. Traditional assessment strategies, such as equal distribution of grades or subjective peer assessments, fall short in terms of fairness, objectivity, and scalability, especially 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, integrating seamlessly with platforms like 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.

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