Call for Papers
Shape the Future of Proactive Conversational AI
We invite original research papers (short and long), perspective papers, system demonstrations, and resource papers that advance our understanding of proactive conversational information seeking. Whether you're developing novel algorithms, discussing potential related future research directions, proposing evaluation frameworks, or building real-world applications, we want to hear from you.
Research Themes
🤖 Core Proactive Behaviors
- Proactive question asking and clarification: How can agents formulate the right questions at the right time to better understand user intent?
- Mixed-initiative dialogue management: What models best balance system initiative with user control in information-seeking conversations?
- Anticipatory information retrieval: Techniques for predicting and surfacing relevant information before users explicitly request it.
🧠 Understanding Users and Context
- User modeling and context understanding: Inferring user goals, knowledge, expertise levels, and preferences to drive intelligent proactive actions.
- Personalized conversational search: Adapting search results, suggestions, and dialogue flow based on individual user characteristics, history, and preferences.
- Cognitive load management: Designing systems that respect users' cognitive capacity and attention limitations while maximizing utility.
- Metacognitive awareness: Building systems that recognize when users need different types of cognitive support or are experiencing confusion.
🔧 Technical Foundations
- LLMs for conversational information access: Retrieval-augmented generation, knowledge grounding, and dynamic topic suggestion in dialogue.
- Memory and forgetting models: Human-like memory mechanisms that prioritize, decay, and reconstruct information for better proactive suggestions.
- Tool orchestration and API integration: Seamlessly coordinating multiple external tools and services for complex, multi-step information needs.
📊 Evaluation and Methodology
- Evaluation metrics and methodologies: Novel benchmarks and user-centric evaluation protocols for measuring proactive system effectiveness.
- Explainability and transparency: Making proactive decisions interpretable and helping users understand system reasoning.
- Long-term interaction studies: Understanding how proactive behaviors affect user satisfaction and task success over time.
🌍 Applications and Impact
- Domain-specific applications: Healthcare, education, customer support, enterprise search, personal assistants, and beyond.
- Multi-modal proactive assistance: Integrating visual, audio, and textual cues across different interaction modalities.
- Cultural and linguistic adaptation: Developing systems sensitive to diverse cultural contexts and communication styles.
⚖️ Responsible AI
- Ethical and user experience considerations: Ensuring proactive behavior is helpful rather than intrusive.
- Privacy and fairness: Protecting user data while ensuring equitable experiences across diverse user populations.
- Bias mitigation: Addressing and preventing harmful biases in proactive suggestions and recommendations.
What We're Looking For
- Novel Research: Cutting-edge algorithms, models, and techniques that advance the state-of-the-art in proactive conversational systems
- Practical Systems: Working implementations, demos, and case studies showing real-world applications of proactive conversational AI
- Theoretical Insights: Position papers, surveys, and conceptual frameworks that help define and structure this emerging field
- Evaluation Studies: User studies, benchmarks, and evaluation frameworks that help us better understand and measure proactive behaviors
- Interdisciplinary Perspectives: Work that bridges computer science with cognitive science, psychology, linguistics, or domain expertise