This work analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering, and characterized courses'learning objectives, assessments, topics, and documented AI tools.
Abstract
As Generative AI coding tools reshape professional software development, universities have begun designing courses to prepare students for AI-assisted development workflows. By analyzing the syllabi of these courses, we can gather empirical evidence about these courses, reveal how this emerging curricular area is being defined, and gain guidance for future curriculum design. We analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering. Through iterative qualitative coding, we characterized courses'learning objectives, assessments, topics, and documented AI tools. Our analysis reveals commonalities and differences among these courses that allow researchers and educators to study and develop future courses.
Generative AI is rapidly transforming workflows in software development. In mechanical engineering education, software skills are taught as a supplemental tool for modeling, but students often lack the experience to leverage AI assistance to approach larger-scale, complex coding tasks. Following a problem-based learning design, 230 third-year students were given a cart-pole system and a series of milestones to achieve. With their choice of generative AI support, they wrote C and Python code to control and analyze data from the cart-pole. We investigated whether this programming challenge would encourage students to adopt programming-specific generative AI strategies, e.g. code editor embedded AI assistants, when presented with the option. Data collected from TAs and student questionnaires indicated that most students continued with default approaches of using chatbots for code generation and debugging. These results indicates that students would benefit from structured training in effective generative AI usage for software coding.
Alan Ableson· Proceedings of the Canadian...· 0 citations
DevJourney is presented, a Generative AI-powered educational game that supports experiential and adaptive learning and highlights the potential of AI-enhanced game-based learning to strengthen software engineering education and better prepare students for industry practice.
Wan Faiz Wan Azman, Aurora Constantin, G. Imperatore· Proceedings of the 2026 Unit...· 0 citations
TPACK-guided scaffolding can align AI affordances with pedagogical goals and RE content, offering design guidance for responsible AI integration in RE education.
Hansika Ekanayake Mudiyanselage, Rohan Jai Dharmaraj, Malik Abdul Sami et al.· arXiv.org· 0 citations
Generative AI tools such as ChatGPT, Claude, and Gemini are increasingly shaping how students engage with software engineering (SE) problem-solving. However, there is limited understanding of how learners collaborate with GenAI across different stages of the software development lifecycle (SDLC), and how learner agency shifts during this process. This research unpacks the problem-solving strategies, collaboration patterns, and agency shifts underlying human–GenAI collaboration in software engineering education. Using a mixed-methods approach, it draws on multimodal data including screen recordings, GenAI transcripts, task artefacts, and retrospective think-aloud interviews. Preliminary findings suggest that learners engage with GenAI differently across SDLC phases, with variation in reliance, evaluation, and decision-making. Future work will extend this analysis to larger and more diverse learner groups and inform pedagogical supports for effective, critical, and agentic GenAI-supported problem-solving.
Sonika Pal· Proceedings of the 2026 ACM...· 0 citations
Generative AI-assisted coding may have introduced a meaningful opportunity for educational researchers with no programming experience to extend their work beyond typical scholarly outputs by creating functional software. Translating research into applications has historically required significant coding expertise, but generative AI has lowered this barrier substantially. Proposed is educational research-driven development (ERDD), an emergent conceptual cross-disciplinary framework combining the educational research process with the software development lifecycle (SDLC) to produce executable scholarship deployable directly to stakeholders. A cross-disciplinary process comparison revealed similarities and differences between the two fields, producing a unified set of scholar-coder workflow steps and two researcher postures: the Sequential Posture, which concludes research before development begins, and the Integrated Posture, which runs both processes in deliberate tandem. Importantly, ERDD has not been validated with rigorous research, and its generalizability has yet to be established. An applied workflow example is demonstrated from the perspective of a non-technical scholar-coder producing a functional web application for educators to record student mental health observational data. The application is accessible at: https://mlittrell-ttu.github.io/mlittrell.github.io/mwb-tracker.html
M. Littrell· International Journal of AI...· 0 citations
This manuscript presents a descriptive study design and preliminary findings from an undergraduate engineering mechanics course conducted in Spring 2026, and details a reproducible survey instrument used to capture student AI usage patterns, attitudes, and verification practices, which are subsequently linked to academic performance metrics.
S. Geng, Helen Lallos-Harrell, Jiya Ashar et al.· arXiv.org· 0 citations
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