*Research Overview: This research delves into the use of external models to enhance human-AI coordination by improving humans’ understandings of AI agents’ intentions. The proposed two-stage training paradigm potentially improves team performance and situational awareness in collaborative scenarios.

Key Findings:

• Improved Coordination: By integrating a Theory of Mind (ToM) model, the research shows enhanced teamwork and communication between humans and AI agents.
• Model Effectiveness: The implementation of a transformer-based predictor improves prediction accuracy, thereby enhancing the coordination process.

Novel Contributions:

• Human-AI Interaction: The study offers new insights into the dynamics of collaboration among humans and AI, emphasizing the importance of intention understanding.
• Practical Applications: The research has significant implications for fields like robotics, interactive systems, and virtual architectural environments where coordination is key.

Opinion and Practice Implications: Enabling effective human-AI interaction through intention understanding could revolutionize how collaborative tasks are approached in various industries. This research presents a novel approach to optimizing collaborative outcomes, making significant strides in human-centric AI technologies.

Further Study: Future studies could focus on refining these interaction models and expanding their applications to more complex scenarios, further enhancing the integration of AI in everyday teamwork contexts.