The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including a working understanding of these systems' fundamental concepts, their limitations, and implications for responsible use. However, misconceptions about GenAI, such as perceiving these systems as mere search engines or database lookup systems, are commonly observed among students, while the availability of teaching resources remains fragmented, and learning objectives lack alignment. This Working Group aims to design pedagogical resources for computing science instructors, enabling them to develop students' GenAI literacy. To achieve this, the Working Group will first identify a concise set of GenAI literacy learning objectives informed by instructor experience, research literature, and community input, and subsequently design pedagogical resources aligned with these objectives.
Bruno Pereira Cipriano, O. Petrovska, Nuno Pombo et al.· Annual Conference on Innovat...· 0 citations
Continuous Integration and Continuous Delivery (CI/CD) pipelines accelerate software evolution but complicate the timely assessment of structural degradation. While code smells effectively indicate maintainability risks, their detection remains largely confined to isolated static analysis rather than being actively integrated into continuous workflows. To address this gap, we propose an explainable AI-driven approach for code smell classification designed specifically for software quality risk analysis in CI/CD environments. Using structural metrics related to complexity, coupling, and cohesion, we train a Random Forest model to classify four prevalent smells: Blob, Long Method, Feature Envy, and Data Class. Evaluated on labeled Java systems, our approach successfully captures the structural patterns indicative of software decay, achieving 82% accuracy and a 0.86 macro F1-score. Beyond raw performance, we operationalize these classifications as interpretable risk indicators within a prototype CI/CD pipeline, prioritizing human-centered evaluation. Ultimately, this work advances the AI for Software Engineering (AI4SE) domain by demonstrating how explainable machine learning can practically augment continuous quality assurance in agile development.
J. Domingues, D. Duarte, António Sousa et al.· International Conference on...· 0 citations
