Date
Publisher
arXiv
Context: As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot
become pervasive in education, concerns are rising about students using them to
complete rather than learn from coursework-risking overreliance, reduced
critical thinking, and long-term skill deficits. Objective: This paper proposes
and empirically applies a causal model to help educators scaffold responsible
GenAI use in Software Engineering (SE) education. The model identifies how
professor actions, student factors, and GenAI tool characteristics influence
students' usage of GenAI tools. Method: Using a design-based research approach,
we applied the model in two contexts: (1) revising four extensive lab
assignments of a final-year Software Testing course at Queen's University
Belfast (QUB), and (2) embedding GenAI-related competencies into the curriculum
of a newly developed SE BSc program at Azerbaijan Technical University (AzTU).
Interventions included GenAI usage declarations, output validation tasks,
peer-review of AI artifacts, and career-relevant messaging. Results: TBD
Conclusions: TBD
Study at a glance
- Question: How can an explanatory model guide responsible generative AI integration in software engineering education?
- Setting and data: Two higher education contexts in the UK and Azerbaijan, including an undergraduate software engineering degree curriculum design and testing course.
- Method: Design-based research with conceptual modeling and qualitative curriculum design analysis.
Why it matters
Generative AI risks encouraging superficial learning, bypassing cognitive effort, and harming integrity. Educators need structured, discipline-aware guidance rather than ad hoc prohibitions to promote students' responsible use.
Key findings
- Generative AI tools helped students complete tasks faster but frequently introduced errors such as hallucinated content, vague logic, and redundant test cases.
- Prompt engineering clarity and specificity directly determined the quality of generative AI outputs, shifting student focus toward strategically steering AI tools.
- Lightweight course revisions, including output validation checklists, usage declarations, and career-oriented messaging, shifted students away from passive reliance toward critical evaluation.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who age?
Why use AI?
Study design
