Date
Publisher
arXiv
In recent years, autonomous AI agents such as GitHub Copilot and Claude Code have been rapidly gaining popularity. This study reports on the practical implementation of Spec-Driven Development, a software development methodology premised on AI agents, within a Software Development Project-Based Learning (SDPBL) course for third-year undergraduate students. We defined a workflow consisting of four phases, namely investigation, planning, implementation, and review. We also established an environment tailored for the SDPBL course where AI agents generate documentation and code during each phase. We analyzed the results from three perspectives, namely students' subjective AI usage, implementation throughput, and code comprehension. The analysis reveals that AI usage patterns varied across development phases and teams. Moreover, while AI agent utilization increased implementation throughput, it also tended to encourage students to proceed with development without fully understanding the code. This study demonstrates that regular verification of code comprehension by instructors and appropriate feedback are essential for maintaining educational effectiveness when introducing SDD into SDPBL.
Why it matters
Autonomous AI agents like GitHub Copilot are rapidly gaining popularity. This highlights the need to examine practical implementations of software development methodologies premised on AI agents within project-based learning courses.
Key findings
- In a third-year college software development course, using AI agents was linked to increased implementation throughput.
- Working with AI agents tended to encourage students to continue developing software without fully understanding the code.
- Students' AI agent usage patterns varied across different development phases and project teams.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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Study design
