TPACK-guided scaffolding can align AI affordances with pedagogical goals and RE content, offering design guidance for responsible AI integration in RE education.
Abstract
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.
Traditional engineering education often relies on delayed summative assessments, struggling to provide the real-time feedback required for sustainable teaching quality improvement. To address this gap, this study proposes a sustainable teaching quality assurance framework leveraging OpenMAIC, an AI-enhanced Multi-Agent Interactive Classroom. A comparative case study was conducted in an undergraduate “Electronic Technology” course. The experimental group utilized the AI-driven curriculum, where an “AI Professor” and “AI Peers” engaged students in heuristic dialogues while generating dynamic HTML5 virtual simulations for immersive circuit experiments. We evaluated the framework’s effectiveness by analyzing fine-grained learning-unit analysis interaction logs, milestone scores, and stakeholder surveys. The results revealed that the multi-agent pedagogical model enabled real-time detection of potential misconceptions based on interaction behaviors, identifying learning bottlenecks weeks earlier than traditional exams. Furthermore, the experimental group significantly outperformed the control group in complex project-based learning tasks, alongside reporting reduced learning anxiety. Instructors also experienced a substantial decrease in repetitive administrative workloads. This study concludes that integrating multi-agent AI and immersive virtual simulations not only enhances continuous process assessment but also establishes a brand-new scalable and continuously improvable teaching paradigm for future digital engineering education.
It is concluded that academic institutions must use clear ethical practices and AI-aware assessment designs to ensure that technology is used as an assisting tool that improves users' learning while ensuring the fundamental values of education.
Sugandha Nandedkar, Prachi N. Waghmare, Ashwini Swami et al.· International Scientific Jou...· 0 citations
The rapid integration of generative artificial intelligence (AI) into higher education raises questions about its pedagogical value and its role in sustainability-oriented learning. This study investigates the use of AI in a Challenge-Based Learning (CBL) engineering hackathon (INDUSHack) focused on the Sustainable Development Goals (SDGs), examining patterns of AI adoption, perceived benefits and risks, and its contribution to SDG integration in project design. A descriptive exploratory design was employed, collecting questionnaire data from 60 undergraduate participants and complementing these with expert committee evaluations of project outcomes. Results indicate widespread AI use, primarily for idea generation, information synthesis, and project structuring. Participants reported perceived improvements in efficiency, creativity, and the clarity of SDG alignment; however, difficulties persisted in translating sustainability concepts into measurable, technically grounded criteria. The findings suggest that while AI can support learning processes and sustainability framing in CBL environments, its effective use requires structured guidance and critical AI literacy to ensure meaningful integration of SDGs.
Sandra Delfa-Baena, Maria Boluda-Prieto, Carla Terron-Santiago et al.· Knowledge· 0 citations
The rapid emergence of generative artificial intelligence (GenAI) is reshaping engineering education. This study investigates the impact of integrating GenAI tools on student learning in the second-year engineering design course at the University of Prince Edward Island by comparing two instructional approaches: a traditional non-AI design project and a GenAI-integrated design project. The study utilized anonymous, voluntary student surveys to evaluate and compare students' learning experiences across both projects. The survey captured both quantitative and qualitative insights into how students perceive the role of GenAI in their learning, collaboration, creativity, and problem-solving processes. Findings show that students most frequently used GenAI for brainstorming, problem definition, and concept generation, while strongly engaging with ethical verification practices. However, perceptions of GenAI’s impact on collaboration and enhancing the efficiency of the design process were mixed. These results inform future learning outcomes, assessment strategies, and broader approaches for responsibly integrating GenAI across engineering programs.
K. Grewal, Mikkayla Ellsworth-Reid, Prabhnoor Sigh et al.· Proceedings of the Canadian...· 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
Generative artificial intelligence (GenAI) technologies such as Claude Code, ChatGPT, and GitHub Copilot are fundamentally reshaping software development practices, shifting the core activities of software engineers from direct code authoring toward validation, orchestration, and architectural reasoning. This paradigm shift raises fundamental questions: what competencies do software engineers require to collaborate effectively alongside GenAI, and how do these requirements vary across career stages? A focused literature review informed by the Systematic Literature Review principles of Kitchenham & Charters (2007) and the mapping study guidelines of Petersen et al. (2015) was conducted to address these questions. Findings were synthesized into a three-pillar competency model: foundational technical competencies augmented by AI tool literacy and prompt engineering; cognitive-analytical skills characterized by intensified critical code review, systems thinking, and AI-generated logic verification; and meta-skills encompassing AI governance, ethical judgment, and continuous adaptability, all of which exhibit significant differentiation across junior, mid-level, and senior software engineers. Furthermore, this study identifies a critical research gap: competency evolution at the senior engineering tier remains substantially under-researched compared to junior and mid-level stages. These findings offer practical implications for software organizations and educational institutions in redesigning competency development pathways in the GenAI era
Muhamad Anggun Novembra· Jurnal Serumpun Teknik Infor...· 0 citations
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