Research Intern: Human-AI Group Interaction

Research Intern:
Human-AI Group Interaction - Honda Research Institute USA

Job Number: P25IN
T-51

Honda Research Institute USA (HRI-US) is seeking a Human-AI Group Interaction Research Intern to study how people collaborate with AI agents in team settings. The intern will support research on AI teammates that can act as peers, facilitators, leaders, or observers during collaborative tasks. The central question is not only whether AI improves task performance, but how AI changes the team process: who participates, who trusts the AI, when the AI helps or disrupts, how workload shifts, and whether the group reaches better shared decisions.

This role is intended for a candidate with strong empirical HCI or human factors research skills. The intern will help design user studies, observe and analyze group interaction, interpret behavioral and survey data, and translate findings into design recommendations for future AI systems. The work is suited for someone interested in human‑AI collaboration, group dynamics, mixed‑methods research, publication‑quality HCI analysis.

The intern will work with researchers and engineers across human‑AI cognitive modeling, simulation, and AI behavior design.

San Jose, CA

• Design and run user studies of human‑AI group collaboration in online and/or in‑person settings.
• Study how different AI teammate roles, such as peer, facilitator, leader, or observer, affect group interaction.
• Develop study protocols, task instructions, surveys, interview questions, and participant‑facing materials.
• Collect and analyze behavioral, survey, communication, and interaction‑log data from collaborative tasks.
• Conduct mixed‑methods analysis that connects quantitative results with qualitative observations and participant feedback.
• Identify patterns in team coordination, trust, workload, participation balance, intervention timing, and group cohesion.
• Translate research findings into clear design implications for adaptive AI teammates.
• Support pilot testing, study deployment, data cleaning, visualization, and research documentation.
• Contribute to internal reports, presentations, and publication‑oriented research outputs.

• Currently enrolled in a Master’s or Ph.D. program in Human‑Computer Interaction, Human Factors, Cognitive Science, Psychology, Computer Science, Information Science, Robotics, Communication, or a related field.
• Experience designing, conducting, or analyzing human‑subject studies, user studies, behavioral experiments, or usability studies.
• Strong understanding of empirical research methods, including experimental design, survey design, and behavioral measurement.
• Experience analyzing quantitative data using Python, R, MATLAB, or similar tools.
• Experience with qualitative or mixed‑methods analysis, such as coding open‑ended responses, analysing interviews, or interpreting observed interaction patterns.
• Strong written communication skills and ability to synthesize research findings into clear insights.
• Research familiarity with human‑AI collaboration, group interaction, AI teammates, or collaborative decision‑making.

• Research experience in human‑AI interaction, human‑robot interaction, CSCW, group dynamics, team cognition, social psychology, or trust in automation.
• Experience analysing team interaction data, such as chat logs, turn‑taking, intervention timing, participation patterns, or group decision processes.
• Familiarity with human factors measures related to trust, workload, usability, perceived usefulness, situation awareness, or team cohesion.
• Experience conducting online studies using tools such as Prolific, Qualtrics, MTurk, or similar platforms.
• Experience preparing research figures, study reports, or academic manuscripts.
• Prior publication record in HCI, AI, robotics, cognitive science, CSCW, or related areas.

Years of Work Experience Required 0

Desired Start Date 8/31/2026

Internship Duration 3 Months

Position Keywords Human‑AI Collaboration, Human‑Computer Interaction, Human Factors, Group Dynamics, Mixed‑Methods User Research, Behavioral User Studies, AI Teammates