AI-Driven Grading and Moderation for Collaborative Projects in Computer Science Education
This addresses fairness and scalability issues in grading for computer science education, though it is incremental as it builds on existing AI and analytics methods.
The paper tackles the challenge of assessing individual contributions in collaborative computer science projects by introducing a semi-automated AI-assisted grading system that uses repository mining and machine learning. A pilot deployment showed high alignment with instructor assessments, increased student satisfaction, and reduced grading effort.
Collaborative group projects are integral to computer science education, as they foster teamwork, problem-solving skills, and industry-relevant competencies. However, assessing individual contributions within group settings has long been a challenge. 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, 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.