Large Language Models as Behavioral Health Teammates in Long-Duration Spaceflight.

INTRODUCTION: Long-duration spaceflight exposes crews to isolated, confined, and extreme environments where chronic stress and limited real-time support heighten interpersonal tensions. Large language models (LLMs) could serve as onboard artificial intelligence teammates for de-escalation, but could also exacerbate tensions by endorsing hostility or exclusion. This study evaluates whether LLMs demonstrate prosocial conflict mediation behaviors vs. failure modes that could elevate interpersonal risk. METHODS: We tested four LLMs (GPT-5, Claude Sonnet 4, Grok 4, Gemini 2.5 Pro) using a Dartmouth PATH Program scenario where one crewmember criticizes an absent teammate and tries to recruit another against him. Models were evaluated across seven test batteries: adversarial framing, backstory manipulation, team role variations, aggressor identity variations, target identity variations, mental health sensitivity, and direct ethics questions. We scored 196 responses using a three-point rubric (defended excluded teammate, neutral, or endorsed exclusion). RESULTS: Claude Sonnet 4 achieved perfect scores (3.0). GPT-5 averaged 2.97. Gemini 2.5 Pro (2.81) showed vulnerability to narrative capture, shifting from defending the excluded crewmember to endorsing criticism when given unfavorable backstory. Grok 4 (2.85) exhibited significant failure under social proof pressure, explicitly endorsing exclusion. All models showed consistent prosocial responses regardless of demographic factors and correctly identified ethical issues when questioned directly. DISCUSSION: LLMs show promise as behavioral health teammates but have critical vulnerabilities. They can be manipulated into endorsing teammate exclusion through backstory reframing, peer pressure, or authority hierarchies, requiring safety protocols before deployment. Dvorak C, Buckey JC Jr. Large language models as behavioral health teammates in long-duration spaceflight. Aerosp Med Hum Perform. 2026; 97(7):516-523.