Ilmu Komputer & AI editorial
Open AccessOA2026
Practical Implementation Report on Introducing Spec-Driven Development Using AI Agents in Software Development PBL
An IMRAD digest of a 2026 study on AI-agent-driven Spec-Driven Development in undergraduate software engineering education
Hidetake Tanaka; Hiroshi Igaki; Kazumasa Shimari; Kiyoshi Honda; Naoki Fukuyasu· 2026· DOI 10.48550/arXiv.2608.30572
The core problem
Autonomous AI agents such as GitHub Copilot and Claude Code have rapidly gained popularity in recent years. This study reports on the practical implementation of Spec-Driven Development (SDD), a software development methodology premised on AI agents, within a Software Development Project-Based Learning (SDPBL) course for third-year undergraduate students. The authors—Hidetake Tanaka, Hiroshi Igaki, Kazumasa Shimari, Kiyoshi Honda, and Naoki Fukuyasu—investigate how SDD can be integrated into an educational setting and what effects it has on student learning. The central research concern is whether AI agents, which can generate both documentation and code, enhance or undermine educational effectiveness in project-based software development courses. The study defines a four-phase workflow and establishes a tailored environment where AI agents generate documentation and code during each phase. The analysis is conducted from three perspectives: students' subjective AI usage, implementation throughput, and code comprehension. This digest summarizes the study's methodology, results, and implications for educators.
Innovation
The analysis reveals that AI usage patterns varied across development phases and teams. This suggests that students adopted different strategies for leveraging AI agents depending on the task at hand—for example, using AI more heavily during implementation than during planning or review. The variation across teams indicates that team dynamics and individual differences influenced how AI was integrated into the workflow. Regarding implementation throughput, the study found that AI agent utilization increased the amount of code produced or features completed. This aligns with the expectation that AI agents can accelerate coding tasks. However, the results also show a concerning trend: AI agent utilization tended to encourage students to proceed with development without fully understanding the code. This indicates a potential trade-off between productivity and deep comprehension. The study does not provide specific numerical values in the abstract, but the qualitative findings highlight the nuanced effects of AI agents on student behavior and learning.
Autonomous AI agents such as GitHub Copilot and Claude Code have rapidly gained popularity in recent years. This study reports on the practical implementation of Spec-Driven Development (SDD), a software development methodology premised on AI agents, within a Software Development Project-Based Learning (SDPBL) course for third-year undergraduate students. The authors—Hidetake Tanaka, Hiroshi Igaki, Kazumasa Shimari, Kiyoshi Honda, and Naoki Fukuyasu—investigate how SDD can be integrated into an educational setting and what effects it has on student learning. The central research concern is whether AI agents, which can generate both documentation and code, enhance or undermine educational effectiveness in project-based software development courses. The study defines a four-phase workflow and establishes a tailored environment where AI agents generate documentation and code during each phase. The analysis is conducted from three perspectives: students' subjective AI usage, implementation throughput, and code comprehension. This digest summarizes the study's methodology, results, and implications for educators.
The authors designed a four-phase workflow for Spec-Driven Development: investigation, planning, implementation, and review. In the investigation phase, students explore requirements and context; in planning, they define specifications; in implementation, they generate code with AI agents; and in review, they verify and refine the output. An environment tailored for the SDPBL course was established, where AI agents generate documentation and code during each phase. The study involved third-year undergraduate students working in teams. Data were collected and analyzed from three perspectives: (1) students' subjective AI usage, likely gathered through surveys or reflections; (2) implementation throughput, measured by the amount of code or features completed; and (3) code comprehension, assessed through instructor verification or student evaluations. The analysis aimed to reveal patterns in AI usage across development phases and teams, and to understand the trade-offs between productivity gains and learning outcomes. The methodology is qualitative and quantitative, combining subjective reports with objective throughput and comprehension measures.
Why it matters
The findings demonstrate that regular verification of code comprehension by instructors and appropriate feedback are essential for maintaining educational effectiveness when introducing SDD into SDPBL. The increased throughput from AI agents is beneficial, but it must not come at the expense of students' understanding. The variation in AI usage patterns suggests that educators should monitor how teams adopt AI and intervene when comprehension is lacking. The study implies that SDD, while promising for simulating modern industry practices, requires careful scaffolding to ensure learning objectives are met. The authors recommend that instructors actively verify code comprehension—perhaps through code reviews, oral examinations, or reflective writing—and provide timely feedback. This study contributes to the growing body of research on AI in software engineering education by offering a practical implementation report and highlighting the need for pedagogical adjustments. Future work could explore quantitative measures of comprehension and long-term retention, as well as strategies to balance AI assistance with deep learning.
Who should read this
CS practitioners and researchers
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