Human-AI Collaboration

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Understanding Role Switching in Human-AI Collaboration through Multimodal Behavioral Signals
Sep 25, 2025Avinash Ajit Nargund, Arthur Caetano, Kevin Yang +7Intent Classification Human-AI Decision Making
University of California, Santa Barbara, USA · Washington University in St.Louis, USA
Agentic Software Engineering: Foundational Pillars and a Research Roadmap
Sep 7, 2025Ahmed E. Hassan, Hao Li, Dayi Lin +4Software Engineering Agents Agentic Workflows
Queen’s University, Kingston, ON, Canada · Concordia University, Montreal, Canada · Nara Institute of Science and Technology, Ikoma, Japan +1
Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks
Aug 6, 2025Sangwon Hyun, Hyunjun Kim, Jinhyuk Jang +2LLM Prompting Code Generation
Adelaide University, Adelaide, SA, Australia · Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation
Aug 5, 2025Wen-Xi Yang, Tian-Fang Zhao, Guan LiuAI in Education Generative AI in Education
Guangdong Institute of Smart Education, Jinan University, Guangzhou, China · School of Journalism and Communication, Jinan University, Guangzhou, China
"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Jun 11, 2025Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei ChooLarge Language Model-Based Role-Play Simulation Emotional Support Conversation
Singapore University of Technology and Design, Singapore
Toward AI standardization: A triadic human-ai collaboration framework for multi-level autonomous mobility
Apr 27, 2025Gaojian Huang, Wei-Hsiang Lo, Guannan Liu +2Human-AI Collaboration
Department of Industrial and Systems Engineering, San Jose State University, San Jose, CA, USA · Department of Applied Data Science, San Jose State University, San Jose, CA, USA
Multimedia and Visual Analytics in the Agentic Era
Apr 8, 2025Marcel Worring, Jan Zahálka, Stef van den Elzen +2Visual Analytics Multimodal Agents
University of Amsterdam · Czech Technical University in Prague · Eindhoven University of Technology +1
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
Mar 26, 2025Huimin Xu, Seungjun Yi, Terence Lim +9Multi-Agent LLMs Clinical NLP
Department of Biomedical Engineering, University of Texas at Austin, USA · McCombs School of Business, University of Texas at Austin, USA · School of Information, University of Texas at Austin, Austin, TX, USA and Dell Medical School, University of Texas at Austin, Austin, TX, USA
Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise
Oct 3, 2024Rose E. Wang, Ana T. Ribeiro, Carly D. Robinson +2Educational Technology Generative AI in Education
Stanford University

Sep 25, 2025J/K move · Enter open · S save

Understanding Role Switching in Human-AI Collaboration through Multimodal Behavioral Signals

Avinash Ajit Nargund, Arthur Caetano, Kevin Yang, Rose Yiwei Liu+6

University of California, Santa Barbara, USA · Washington University in St.Louis, USA

Intent Classification Human-AI Decision Making Human-AI Collaboration

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Human-AI collaboration often requires dividing complex tasks into complementary subtasks. As a task unfolds, users may want to shift which subtasks they perform and which their AI partner performs, in response to evolving task demands and perceptions of the AI's capabilities. In this work, we investigate whether behavioral signals can reveal such changes during a sequential decision-making task. We conducted a study using hand-and-brain chess, where, on each turn, participants chose either to select the piece type (brain) while their AI partner chose the move (hand), or to choose the move after the AI selected the piece type. Across 21 chess players, this yielded more than 1,100 decisions to retain their role from the previous turn or switch to the other role. Players generally retained their current roles across turns. When participants did switch, they exhibited more exploratory gaze patterns and role switches were associated with lower subsequent move quality. Using these behavioral and task-specific signals, we trained a classifier to distinguish switch from stay decisions, achieving a PR-AUC of 0.56 (compared to a random baseline of 0.39). Feature-set ablations showed that gaze and task-specific features contributed most to model performance. Role switching was not a simple choice between controlling and delegating the task as both roles required participants to perform one subtask and delegate the other. However, interviews showed that many participants perceived selecting the piece type as giving them greater control. Perceived AI ability, relative subtask difficulty, and desired influence over the direction of play shaped these role preferences. These findings suggest that behavioral cues could help intelligent systems recognize when users want to reallocate complementary responsibilities during collaboration.