In today’s workplaces, digital distractions and frequent task-switching significantly impact productivity and well-being. With the integration of AI tools and Large Language Models (LLMs), we face new challenges in managing attention between human cognition and AI-assisted workflows. This workshop will explore the dual nature of AI tools, examining their potential to enhance productivity and mitigate the risks of "metacognitive laziness" and digital distraction. Through collaborative design activities and discussions, participants will examine strategies for developing attention management that support mindful task-switching between human and AI-driven work. We will focus on designing workplaces that leverage AI capabilities while preserving deep work and cognitive well-being. The workshop aims to generate practical insights for creating sustainable work practices in an AI-augmented workplace.

Research on cycling safety has recently gained the attention of the HCI community. While there have been multiple proposals for automated driving features on bikes, we are unaware of a project that systematically aims to translate and evaluate driver assistance systems from the automotive to the bike domain to promote cycling safety in traffic. Thus, we implemented an adaptive cruise control and a lane-keeping/centering system with hard- and software on a motion-based bicycle simulator and investigated their potential in a virtual reality experiment. Based on performance measurements and subjective ratings, results showed significant improvements in technology acceptance, subjective workload, and driving performance regarding the cruise control. In contrast, the lane-centering and lane-keeping features were rated significantly worse than the baseline without such assistance. The paper concludes with a critical reflection on automated driving features for bicycles.

Attention management systems seek to minimize disruption by intelligently timing interruptions and helping users navigate multiple tasks and activities. While there is a solid theoretical basis and rich history in HCI research for attention management, little progress has been made regarding their practical implementation and deployment. Building sophisticated attention management systems requires a great variety of sensors, task- and user models, and multiple devices while considering the complexity of user context and human behavior. Novel AI technologies, such as generative systems, reinforcement learning, and large language models, open new possibilities to create intelligent, practical, and user-centered attention management systems. This proposed workshop aims to bring together researchers and practitioners from diverse backgrounds to discuss and formulate a research agenda to advance attention management systems using novel AI tools to manage and mitigate interruptions from computing systems effectively.