How Do Software Engineering Students Use Generative AI in Real-World Capstone Projects? An Empirical Baseline Study

• URL: http://arxiv.org/abs/2604.24521v1
• Date: Mon, 27 Apr 2026 14:23:46 GMT
• Title: How Do Software Engineering Students Use Generative AI in Real-World Capstone Projects? An Empirical Baseline Study
• Abstract summary: The rapid integration of generative AI (GenAI) into professional software development adds new challenges.<n>We conducted a large-scale study of self-determined GenAI use in an undergraduate RWCP course.<n>Our findings provide a characterization of GenAI practices across the software engineering lifecycle.
• Score: 1.5102168012018966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world Capstone Projects (RWCPs) are a key component of software engineering education, enabling students to develop software for external clients under authentic conditions. Their high ecological validity, combined with substantial variation in domains, technologies, and stakeholders, typically requires flexible and minimally prescriptive teaching approaches. The rapid integration of generative AI (GenAI) into professional software development adds new challenges: students are expected to use AI tools that are common in practice, yet unguided use may affect learning, collaboration, and consistency in ways that are not yet well understood. To establish an empirical baseline for responsible GenAI integration, we conducted a large-scale study of self-determined GenAI use in an undergraduate RWCP course. The module involved 178 students working in 18 teams across 15 client projects over four months, with GenAI use explicitly permitted. We collected mixed-method survey data from 150 students on attitudes, usage prevalence, workflows, use cases, and perceived benefits and risks, and surveyed client stakeholders regarding expectations and concerns. Our findings provide (1) a characterization of GenAI practices across the software engineering lifecycle, including a distinction between emerging workflows; (2) student-recommended use cases and responsible-use directives emphasizing verification and maintaining independent understanding; (3) client perspectives highlighting strong support for GenAI use but clear expectations regarding understanding, quality, and data protection; and (4) implications for future course iterations, including the need for explicit responsible-use guidelines, targeted AI literacy resources, and team-level governance roles. This study offers a status quo baseline for evidence-based pedagogical interventions in the era of GenAI.

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