Real-time collaborative dialogue tasks require dynamic, instantaneous decision-making and seamless coordination between participants, yet most existing studies on cooperative dialogues primarily focus on turn-based textual environments. This study addresses the critical gap in understanding human-human interaction patterns within dynamic, real-time collaborative scenarios. In this paper, we present a novel dataset collected from a real-time collaborative cooking game environment inspired by the popular game “Overcooked.” Our dataset comprises detailed annotations of participants’ task proficiency levels, game scores, game action logs, and transcribed voice dialogues annotated with dialogue act tags. Participants exhibited a broad range of gaming experience, from highly proficient players to those with minimal exposure to gaming controls. Through comprehensive analysis, we explore how individual differences in task proficiency influence dialogue patterns and collaborative outcomes. Our findings reveal key dialogue acts and adaptive communication strategies crucial for successful real-time collaboration. Furthermore, this study provides valuable insights into designing adaptive dialogue systems capable of dynamically adjusting interaction strategies based on user proficiency, paving the way for more effective human-AI collaborative systems. The dataset introduced in this study is publicly available at: https://github.com/UEC-InabaLab/OverCookedChat.

My research interests lie in human-agent collaboration and user adaptation, with a particular emphasis on adaptation in real-time collaborative environments.The field of collaborative systems aims to support human teams in completing complex tasks efficiently while ensuring natural and adaptive interaction experiences.I investigate how AI agents can function as effective partners by adapting to their human collaborators.A central focus of my research is the personalization of agent behavior based on user proficiency.This includes methods for adapting the agent’s communication strategies according to the user’s skill level and task experience. To pursue this goal, I collected and analyzed a multimodal dataset of human-human interaction using a real-time collaborative cooking game environment (Wu et al., 2021; Liu et al., 2024).The chosen environment is characterized by its complex task mechanics and strict time constraints, which necessitate seamless coordination and elicit dynamic, natural collaborative behaviors such as role negotiation and error recovery.Through this analysis, I investigated how partners with different levels of task proficiency communicate and coordinate effectively.Based on the findings, I proposed practical design guidelines for future adaptive AI agents, enabling them to adjust their level of guidance and initiative in response to the user’s proficiency.