2026-09-01
Course page and Lectures 1–4 published
Slides are available for orientation, development processes and AI tools, prompt writing, and the boundary of AI delegation.
Generative AI Software Engineering
Learn the human responsibilities of problem definition, requirements, design, validation, and accountability when AI generates code. Weekly exercises grow into a deployed team project.
Operating principle: Use AI tools actively, but
be able to explain and validate the behavior and decisions in
every submission.
Notices
Announcements
2026-09-01
Preparation
Bring a laptop and prepare GitHub and ChatGPT accounts. Installation and tool connections will be completed in class.
Lecture Materials
Current Slide Decks
| Lecture | Topic | Focus | Material | Status |
|---|---|---|---|---|
| 1 | Orientation & Software Engineering | Course rules, software crises, environment setup, Git/GitHub | PPTX | Open |
| 2 | Development Processes & AI Tools | Process models, Agile/Scrum, Codex and ZCode setup | PPTX | Open |
| 3 | Generative AI & Prompting | Probabilistic generation, role, context, requirements, format | PPTX | Open |
| 4 | The Boundary of AI Delegation | Delegability, task decomposition, human checks, accountability | PPTX | Open |
Backup slide versions are excluded; only current materials are linked.
Semester Roadmap
16-Week Learning Path
Foundations & Delegation
Set up tools, understand generative AI, write effective instructions, and determine what can be delegated.
Requirements & Design
Define the team project, develop requirements and architecture, and implement from specifications.
Quality & Operations
Testing, debugging, maintenance, deployment automation, coding agents, security, and ethics.
Deployment & Retrospective
Demonstrate a deployed product and reflect on the project's evidence and decisions.
Contact
Course Inquiries
Email: sangdonpark@dju.kr
Subject:
[Generative AI SE-01][Student ID] Topic
The university LMS remains authoritative for assignments
and attendance.