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 effectively manage and mitigate interruptions from computing systems.
Attention Management Systems (AMS): Systems that sense, model, and manage the attentional state of a user. Managing the attentional state is considered as any system action that supports an individual to maintain concentration on a task or activity (Anderson, 2018)
Attentive User Interfaces: Computer interfaces that are sensitive to the user’s attention; measure and model the focus and priorities of attention; structuring communication such that the limited resource of attention is allocated
optimally across the user’s tasks (Vertegaal, 2003).
Pervasive Computing: Environment saturated with computing and communication capability gracefully integrated with users; minimal user distraction; interaction almost at a subconscious level; seamless integration of devices/services, automatically adjusting behavior to fit circumstances.
Pervasive Attentive User Interfaces: Continuously manage users’ attention in daily life, simultaneously optimizing for both information throughput and
subtlety (Bulling, 2016).
see Relevant Publications for further readings
Workshop Goals and Questions
- How can AMSs empower humans? How can we build technology to help individuals navigate modern information overload and achieve effective task accomplishment? How can we guarantee that AMSs satisfy human needs such as feeling autonomous, competent, and meaningful? How do AMS's features depend on life situations?
- What limitations must be considered? How do people feel when AI decides whether something is important and demands immediate attention? How do we responsibly balance performance optimization with mental well-being?
- How to use novel AI tools? Can we detect task boundaries with AI? Can language models or generative AI build task resumption cues on the fly?
- What are new opportunities due to advances in AI? How can we support robust real-time monitoring and prediction of cognitive states? How can we design collaborative attention management?
- What are the long-term implications? How will AI-supported attention management affect users' inherent attention management skills, productivity, and well-being?
- What are the areas of applications for AMS? How could we categorize AMSs according to their characteristics (i.e., time, risk, etc.)? Which specific requirements can be derived from these classifications?