Sep 2026· Proceedings of the 2026 United Kingdom and Ireland Computing Education Research· 0 citations· 2 references
TL;DR
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.
Abstract
Computer science graduates often struggle to apply software engineering theory to practice. Skills such as applying Agile methodologies, working with industry tools, and understanding end-to-end software development workflows remain challenging to acquire through traditional coursework alone. This paper presents DevJourney, a Generative AI-powered educational game that supports experiential and adaptive learning. Grounded in Bloom’s Taxonomy and informed by empirical findings from students, lecturers, and software engineers, the system simulates key stages of the Software Development Life Cycle using AI-generated scenarios and feedback. A summative evaluation revealed positive perceptions of usability, engagement, and learning support, highlighting the potential of AI-enhanced game-based learning to strengthen software engineering education and better prepare students for industry practice.
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.
Francis Geng, Anshul Shah, Miannuan Chen et al.· 0 citations
The rapid development of generative artificial intelligence (AI) risks widening gaps in digital literacy, critical thinking skills, and cognitive problem-solving, particularly for undergraduate students who will encounter AI tools in academic and professional contexts. Critical thinking is also identified as a key competency in engineering practice, with recent studies raising concerns about how AI might affect students’ problem-solving skills. The AI Client for Engineering (ACE) was designed to support critical thinking and discussion in engineering design courses. This paper outlines the development process and customizable alignment with pedagogical outcomes. In a pilot implementation, students used ACE as a tool for asking questions, seeking guidance, and simulating real-world interactions within an engineering organization. Through this integration, ACE aims to provide students with knowledge and practical experience, preparing them for future challenges and opportunities in academic, professional, and industry contexts.
Elias Poitras-Whitecalf, Emily Marasco, Keeryn Johnson· Proceedings of the Canadian...· 0 citations
Software engineering education should balance theoretical concepts with practical application, and many curricula adopt practical skills training organised around project-based learning. In this context, the choice of target software systems becomes critical to the success of the learning experience. Traditional software engineering modules in higher education often provide projects with clearly defined requirements, removing much of the authentic ambiguity found in real world settings - ambiguity that requires engineers to define problems, navigate shifting requirements, and adapt to unforeseen changes. To address these limitations, we introduced an ideation process into our project-based learning approach. This process helps students cope with uncertainty and develop skills needed to operate in scenarios that more accurately mirror professional practice. Analysis of student feedback from reflective reports shows strong appreciation for this approach, particularly regarding guided learning, inclusion of students with diverse backgrounds and skill sets, improved skills development in negotiation, pitching, and presenting, and a stronger sense of project ownership. We also encountered challenges, including balancing project workload with existing commitments, ensuring fairness when the selected project theme does not match available resources, and distributing workload equitably within teams. As future work, we aim to strengthen the ideation process by providing clearer guidance on project scoping and workload estimation.
Jaejoon Lee, S. Nicholson, Kay Bond· Annual Conference on Innovat...· 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
The rapid adoption of generative Artificial Intelligence (AI) in software engineering (SE) practice creates a need for pedagogically grounded approaches to AI integration in SE education, especially in conceptually intensive subjects such as requirements engineering (RE). This study examines a TPACK-guided integration of a multi-agent AI tool into a master-level RE assignment on requirements quality analysis. Using a mixed-methods design (N=100; 72 submissions analysed), we examine how structured assignment design shaped students'AI use, affected their understanding of user story quality criteria, and influenced their perceptions of AI's benefits and limitations. Results show that students used the AI tool selectively, mainly as support for analysis and evaluation rather than automation. Alignment improvements were most evident for structurally concrete requirements quality dimensions, such as value articulation and testability, while negotiability showed mixed effects. Students reported conditional trust, active refinement, and increased awareness of quality criteria, alongside moderate usability challenges. The findings show that 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.· 0 citations
This paper confronts the critical “implementation gap” in software engineering education by charting a practical path from a theoretical micro-credential blueprint to classroom reality. While a suite of ten stackable credentials mapped to the software development lifecycle offers a promising solution for developing professional competencies, its value is only realized through successful integration into the complex ecosystem of higher education. This study addresses the pedagogical, logistical, technological, and cultural challenges of such an undertaking by proposing three distinct integration models—Embedded, Semester-Themed, and Dual Academic-Industrial pathways—along with a phased implementation roadmap. By providing curriculum designers and educators with actionable strategies to navigate institutional constraints, this work transforms an innovative design into a tangible framework for building the next generation of software engineering professionals.
Franklin Parrales-Bravo, Roberto Tolozano-Benites, Lorenzo Cevallos-Torres· 2026 6th International Confe...· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
