2026 ASEE Annual Conference & Exposition

The capstone project in Computer Science and Engineering disciplines typically serves as the culminating experience and final integrative component of an academic program. Its primary objective is not only to solidify students’ disciplinary expertise but also to provide authentic and hands-on experience in Computer Science and Engineering disciplines. By engaging students in applied, often industry-driven projects, capstone courses cultivate essential professional skills such as communication, teamwork, and project management that significantly enhance graduates’ preparedness for the workforce. Despite these benefits, the design, implementation and assessment of meaningful capstone and project-based learning experiences present significant challenges. Faculty must balance multiple learning objectives while ensuring project quality and success, managing large student cohorts, facilitating open-ended projects, and assessing individual contributions equitably. Effective capstone design must ensure that students not only apply technical skills but also develop key professional competencies in project planning, communication, collaboration, and teamwork. Teamwork remains a cornerstone for capstone and project-based learning models, valued for its ability to replicate professional environments where collaboration is essential. Group projects enhance academic learning by fostering problem-solving, cooperation, and collective decision-making. However, teamwork in group projects also introduces challenges that may lead to student dissatisfaction. Common issues include logistical coordination, interpersonal conflicts, and uneven participation, with some students contributing disproportionately less while relying on peers to complete the work. Such inequities undermine both fairness and the quality of student learning.

To address challenges in teamwork, peer evaluation has been widely adopted as a mechanism to promote student accountability and ensure a fair distribution of effort among team members. Yet, peer evaluations alone may not fully capture the complexities of team dynamics or accurately reflect individual contributions. To strengthen both accountability and transparency, this paper explores the use of meeting logs as an additional data source for assessing participation. Specifically, we examine the integration of ChatGPT to analyze weekly team meeting logs and generate reports and contribution scores for individual team members based on predefined criteria. A range of predefined criteria is tested to evaluate how different inputs affect the accuracy, consistency, and perceived fairness of AI-generated assessments. The findings indicate that integrating peer evaluation with AI-assisted analysis of team meeting logs can enhance transparency, provide a verifiable record of participation, and support more equitable assessment practices. Ultimately, this hybrid approach has the potential to strengthen accountability, improve team dynamics, encourage individual contribution while fostering collaboration, and enrich the overall capstone learning experience in higher education.