Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings remains challenging. Traditional approaches, including equal grade distribution and subjective peer evaluations, often lack fairness, objectivity, and scalability, particularly in large classrooms. We propose TRACE, a semi-automated AI-assisted framework for assessing collaborative software projects that evaluates both project quality and individual contributions using repository mining, communication analytics, and AI-assisted analytics. A pilot deployment in a software engineering course demonstrated high alignment with instructor assessments, increased student satisfaction, and reduced instructor grading effort. The results suggest that AI-assisted analytics can improve the transparency and scalability of collaborative project assessment in computer science education.

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、问题解决及行业相关技能。然而，在小组环境中评估个人贡献仍然具有挑战性。传统方法，包括平均分配成绩和主观的同伴互评，往往缺乏公平性、客观性和可扩展性，尤其是在大规模课堂中。我们提出了TRACE，一个半自动化的AI辅助框架，用于评估协作软件项目。该框架通过仓库挖掘、沟通分析和AI辅助分析，同时评估项目质量和个人贡献。在软件工程课程中的试点部署表明，该框架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。结果表明，AI辅助分析能够提升计算机科学教育中协作项目评估的透明度和可扩展性。