Songmei Yu, Andrew Zagula

Proceedings of the 19th International Multi-Conference on Society, Cybernetics and Informatics: IMSCI 2025, pp. 6-11 (2025); https://doi.org/10.54808/IMSCI2025.01.6

Songmei Yu
Department of Computer Science, School of Business and Information Sciences, Felician University, Rutherford, New Jersey, United States

Andrew Zagula
Bridgewater-Raritan High School, Bridgewater, New Jersey, United States

Cite this paper as:
Yu, S., Zagula, A. (2025). AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education. In N. Callaos, J. Horne, B. Sánchez, M. Savoie (Eds.), Proceedings of the 19th International Multi-Conference on Society, Cybernetics and Informatics: IMSCI 2025, pp. 6-11. International Institute of Informatics and Cybernetics. https://doi.org/10.54808/IMSCI2025.01.6

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.