Team-based projects are a cornerstone of engineering and computing courses, but unstructured team formation often leads to poor project outcomes due to misaligned student interests and inadequate skill coverage. This paper introduces a novel, three-stage methodology for creating effective student teams by integrating student preferences with project skill requirements. In the first stage, students complete a survey to report their project interests and self-assessed skills. Next, a Large Language Model (LLM) analyzes project descriptions to extract the necessary skills for each project's success. Finally, a dynamic assignment algorithm matches students to projects, simultaneously maximizing skill coverage and preference alignment. The algorithm iteratively prioritizes projects with unfulfilled skill needs to optimize team balance. Preliminary evaluations show our approach produces teams with higher skill coverage and better preference satisfaction compared to random or manual assignment approaches. Our approach also overcomes limitations of widely-used tools like CATME Team-Maker, which do not explicitly account for project skill fulfillment. Our findings point toward an effective and customizable strategy for improving student motivation and learning outcomes in project-based courses.

翻译：团队项目是工程与计算机课程的核心环节，但无序的团队组建常因学生兴趣错位与技能覆盖不足导致项目成果不佳。本文提出一种创新的三阶段方法论，通过整合学生偏好与项目技能需求来创建高效团队。第一阶段，学生完成问卷调查，报告其项目兴趣与自评技能；第二阶段，大语言模型（LLM）分析项目描述，提取各项目成功所需的关键技能；第三阶段，动态分配算法将学生与项目进行匹配，同步实现技能覆盖与偏好对齐的最大化。该算法通过迭代优先处理存在技能缺口的项目，以优化团队平衡。初步评估表明，相较于随机或人工分配方式，本方法组建的团队具有更高的技能覆盖度与更好的偏好满意度。本方法还克服了CATME团队组建工具等广泛使用工具的局限——这些工具未明确考虑项目技能需求的满足情况。研究结果为改善项目式课程中的学生动机与学习成效，提供了一种有效且可定制的策略。