Call for Papers

We welcome research papers, benchmark papers, datasets, systems papers, position papers, and interdisciplinary studies on human modeling, AI metacognition, and interaction-level decision making in agentic systems.

Central Question

How should AI systems use uncertain models of humans and themselves to select interaction policies?

Topics of Interest

• Human state modeling, including theory of mind, knowledge and uncertainty modeling, trust, cognitive load, goals, and intent

• AI metacognition, including capability boundary assessment, selective prediction, abstention, reliability estimation, and out-of-distribution self-assessment

• Interaction policy learning, including clarification, human-aware planning, escalation, and deferral

• Personalization and longitudinal adaptation

• Evaluation and benchmarks, including human-centered metrics and trust calibration

• Applications in education, healthcare, robotics, AI companions, scientific discovery, and recommendation

Submission Types

• Research papers

• Position papers

• Benchmark papers

• Dataset papers

• Systems papers

• Interdisciplinary studies

Formatting

• Main text up to 9 pages, excluding references and appendices.
• Double-blind peer review.
• Non-archival proceedings.
• Authors retain the right to submit extended versions to archival venues.

Review Criteria

• Relevance to the workshop theme

• Scientific quality and technical soundness

• Novelty and significance

• Potential impact on human-AI interaction and agentic systems

• Clarity of presentation

• Reproducibility, benchmarking, or evaluation rigor where appropriate

Submission Site

TBC