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.

翻译：协作式小组项目是计算机科学教育的重要组成部分，能够培养团队合作、问题解决能力以及与行业相关的技能。然而，在小组环境中评估个人贡献长期以来一直是一项挑战。传统的评估策略，例如平均分配分数或主观的同伴评估，通常在公平性、客观性和可扩展性方面存在不足，尤其是在大型课堂中。本文介绍了一种半自动化、人工智能辅助的评分系统，该系统利用仓库挖掘、通信分析和机器学习模型来评估项目质量和个人贡献。该系统包含项目评估、贡献分析和分数计算模块，并能与GitHub等平台无缝集成。在一个高年级课程中的试点部署表明，该系统与教师评估高度一致，提高了学生满意度，并减少了教师的评分工作量。最后，我们讨论了实施注意事项、伦理影响以及为扩大适用性而提出的改进建议。