Summary
Software engineering students today use AI tools to write code, but this creates new challenges in making sure they really understand what the AI produces. The authors reviewed a lot of research and practice about how AI affects learning in software engineering from 2023 to 2026. They found that AI helps with some tasks like giving explanations and feedback, but students still need guidance and ways to check if AI-generated code is correct. They introduced a framework to help educators design learning that balances using AI and making sure students can explain, verify, modify, and account for their software.
What this means in practice
- •For software engineering educators: Use the AI-Augmented Software Engineering Education framework to design courses that maintain student understanding while integrating AI tools responsibly.
- •For software development team leads: Adopt practices addressing comprehension debt to manage risks from AI-generated code persisting in repositories and production systems.
A survey. It maps existing work.
Abstract
Generative artificial intelligence has moved from a disruptive novelty to a recurring part of software-development and computing-education workflows, while software agents are beginning to act across repositories, command lines, browsers, tests, and other tools. The educational problem is no longer whether students should be allowed to generate code, but whether software-engineering (SE) programs can preserve and assess human understanding while preparing students to work responsibly with increasingly capable AI systems. This paper presents a structured integrative review of research and practice from 2023 through 23 September 2026, supplemented by established work on AI literacy, technical debt, and human-AI collaboration. The evidence supports a conditional conclusion: GenAI can improve access to explanations, feedback, practice, and short-term task completion, but learning outcomes depend on prior knowledge, scaffolding, verification, task design, and assessment. We examine student help-seeking and authorship, faculty assessment and policy demands, and the wider SE risks created when generated artifacts persist across repositories, teams, architectures, and deployed systems. We extend the concept of comprehension debt: the deferred learning and maintenance cost that arises when AI-assisted production outpaces a learner's or team's ability to explain, test, modify, and justify the resulting software. We then refine the AI-Augmented Software Engineering Education (AASEE) framework into five non-linear integration levels and four cross-cutting evidence obligations: explain, verify, modify, and account. The framework links delegation to evidence, governance, and recovery mechanisms appropriate to its consequences.