This paper presents a model showing that while individually beneficial, widespread adoption of generative AI can reduce social welfare by degrading future model performance through "model collapse," especially for high-incentive tasks. It highlights a critical social dilemma where short-term gains lead to long-term collective losses.

This paper provides a comprehensive literature review on the application of Large Language Models (LLMs) in business process modeling, detailing their current integration into text-to-model pipelines and identifying key challenges and future research directions for supporting complex organizational process modeling.

This paper explores lightweight Generative AI (GenAI) models for network traffic synthesis to address data scarcity and privacy in Network Traffic Classification (NTC). It evaluates transformer-based, state-space, and diffusion models, demonstrating their effectiveness in generating high-fidelity synthetic traffic for training and augmenting NTC systems.

This paper proposes a theoretical extension of the knowledge gap hypothesis to understand emerging forms of informational inequality driven by generative AI. It argues that while access to AI is widespread, disparities will arise from users' critical evaluation skills when interacting with AI-generated content, with higher education fostering more critical engagement.

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.