Proactive Systems in HCI and AI: Concepts, Challenges, and Opportunities

A multidisciplinary workshop brings together HCI and AI researchers to establish clearer definitions and frameworks for proactive systems—autonomous technologies that anticipate user needs and act without explicit input. The effort addresses conceptual ambiguity in how proactivity is currently defined and applied across different domains, while identifying gaps in design and evaluation methodologies that remain rooted in reactive paradigms.

The rise of autonomous and proactive AI systems has created a definitional crisis within the research community. While applications range from smart home devices to assistive robots, the term 'proactivity' remains loosely applied, with fundamentally different behaviors—from simple reminders to advanced predictive actions—conflated under the same umbrella. This conceptual muddiness hampers systematic comparison and evaluation of these technologies.

The problem stems from the field's rapid expansion outpacing conceptual rigor. Most design and evaluation methodologies originated in reactive systems where users explicitly trigger actions. Proactive systems introduce novel challenges: determining optimal timing for interventions, assessing appropriateness of unsolicited actions, maintaining meaningful user control, ensuring transparency about system reasoning, and building trust when systems act autonomously. Current frameworks fail to address these dimensions coherently.

For the AI and HCI industries, this workshop represents a necessary consolidation effort. Clearer definitions enable better benchmarking, more meaningful comparisons between competing approaches, and standardized evaluation criteria. Developers gain concrete guidelines for designing trustworthy proactive features, while researchers establish shared terminology that accelerates knowledge transfer. The emphasis on human-centered design acknowledges that technical capability alone is insufficient—user acceptance depends on addressing control, transparency, and appropriateness concerns.

Looking forward, the development of rigorous frameworks will directly influence how proactive AI features roll out across consumer and enterprise applications. Organizations currently deploying autonomous systems without established design principles face user adoption risks. Success requires convergence around best practices, making this workshop's collaborative output potentially foundational for the next generation of AI product design standards.

• →Current usage of 'proactivity' conflates fundamentally different system behaviors, limiting systematic design and evaluation.
• →Existing design methodologies remain rooted in reactive paradigms and fail to address unique challenges like timing, appropriateness, and trust.
• →Key challenges for proactive systems include managing user control, ensuring transparency, and building appropriate trust levels.
• →The workshop aims to establish shared definitions and human-centered guidelines across AI, HCI, and related disciplines.
• →Standardized frameworks for proactive systems will improve product design consistency and user adoption across industries.