AreWe On Track? AI-Assisted Active and Passive Goal Reflection During Meetings

2025-04-01

AI Summary

Ambiguous meeting objectives lead to off-topic discussions and low efficiency, while existing tools lack effective support for intentional goal alignment. Method: This study proposes an AI-augmented goal reflection paradigm, featuring two complementary mechanisms—passive (implicit prompting) and active (explicit intervention)—to enable knowledge workers to identify, clarify, and recalibrate meeting goals in real time. We introduce a novel three-dimensional design framework addressing intervention intensity/timing, democratic participation versus efficiency, and user agency, validated via a technology probe study grounded in authentic meeting data and integrating natural language understanding, dialogue state modeling, and human-AI collaborative feedback. Contribution/Results: Goal clarity is foundational to effective reflection; passive interventions enhance focus without interruption, whereas active interventions drive immediate action but risk disruption. The study yields transferable design principles for AI-mediated reflective collaboration, advancing both theoretical foundations and practical implementation of intelligent meeting systems.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Mechanism Design

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Meetings often suffer from a lack of intentionality, such as unclear goals and straying off-topic. Identifying goals and maintaining their clarity throughout a meeting is challenging, as discussions and uncertainties evolve. Yet meeting technologies predominantly fail to support meeting intentionality. AI-assisted reflection is a promising approach. To explore this, we conducted a technology probe study with 15 knowledge workers, integrating their real meeting data into two AI-assisted reflection probes: a passive and active design. Participants identified goal clarification as a foundational aspect of reflection. Goal clarity enabled people to assess when their meetings were off-track and reprioritize accordingly. Passive AI intervention helped participants maintain focus through non-intrusive feedback, while active AI intervention, though effective at triggering immediate reflection and action, risked disrupting the conversation flow. We identify three key design dimensions for AI-assisted reflection systems, and provide insights into design trade-offs, emphasizing the need to adapt intervention intensity and timing, balance democratic input with efficiency, and offer user control to foster intentional, goal-oriented behavior during meetings and beyond.

Problem

• AI helps clarify meeting goals for better focus
• Passive vs active AI interventions balance reflection and flow
• Design dimensions for AI-assisted goal-oriented meeting systems

Innovation

• Passive AI maintains focus non-intrusively
• Active AI triggers immediate reflection
• Balances intervention timing and user control