This paper introduces the Memory Printer, a tangible device combining slow design and generative AI for reconstructing unrecorded personal memories. It explores how embodied, layered interactions can enhance user agency and emotional engagement in reminiscing, while also highlighting critical concerns regarding false memories, algorithmic bias, and data privacy in emotionally sensitive AI applications.

This paper introduces a teaching case that uses generative AI personas in a stakeholder simulation to educate students on Agile Requirements Engineering. It aims to provide practical experience in requirements elicitation and documentation while fostering critical reflection on the limitations and ethical considerations of AI.

This paper introduces a novel neural network framework that combines a discrete noise graph diffusion model with an autoregressive solver to enhance solutions for Vehicle Routing Problems (VRPs). By learning and integrating problem constraints through a generated constraint matrix, the approach improves robustness and achieves state-of-the-art performance on various benchmarks.

This paper investigates how physics students utilize generative AI (genAI) for computational modeling in open-ended assignments, revealing its significant impact on planning, implementing, and debugging code. It highlights both the efficiency gains and the risks to learning when students over-rely on genAI without critical verification.

This paper explores STEM faculty perspectives on the integration of Generative AI in higher education through a focus group study. It investigates how faculty use GenAI in courses, the perceived benefits and challenges for student learning, and necessary institutional support, highlighting shifts in pedagogy, assessment, and governance.

This paper discusses the challenges and opportunities for statistics education in adapting to a rapidly evolving technological landscape. It explores the integration of modern programming languages, data practices, machine learning, and artificial intelligence, including generative AI, into university curricula.

This paper explores the application of generative AI, specifically conditional flow matching, for the inverse design of ship propellers. It aims to generate multiple propeller geometries that achieve specified performance targets, overcoming limitations of traditional forward models and addressing data scarcity through simulated data and augmentation.

This paper introduces CM-GAI, a continuum mechanics-based theoretical framework that extends optimal transport theory to describe data dynamics. It enables generative tasks with limited data by integrating physical consistency, successfully demonstrated across material, structural, and system-level mechanical engineering problems.

This paper introduces and evaluates a novel Rhetorical Structure Theory (RST)-based in-context learning method to improve the quality of AI-generated educational diagrams, finding that while it reduces hallucinations, LLMs still struggle with complex inputs and require careful application in educational contexts.

This paper introduces EndoRare, a novel one-shot generative framework that synthesizes diverse, high-fidelity images of rare gastrointestinal lesions from a single reference. It significantly enhances the diagnostic accuracy of AI models and improves the training of novice clinicians by providing realistic and varied case examples.