Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications

Abstract: 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: In the course-level case, instructor observations and student artifacts indicated increased critical engagement with GenAI, reduced passive reliance, and improved awareness of validation practices. In the curriculum-level case, the model guided integration of GenAI learning outcomes across multiple modules and levels, enabling longitudinal scaffolding of AI literacy. Conclusion: The causal model served as both a design scaffold and a reflection tool. It helped align GenAI-related pedagogy with SE education goals and can offer a useful framework for instructors and curriculum designers navigating the challenges of GenAI-era education.