Large Language Models as Behavioral Health Teammates in Long-Duration Spaceflight
Clinical Snapshot
PICO Framework
| P — Population | Large language models (GPT-5, Claude Sonnet 4, Grok 4, Gemini 2.5 Pro) evaluated as proxies for onboard behavioral health support systems in simulated long-duration spaceflight crew conflict scenarios |
| I — Intervention | Exposure of LLMs to structured conflict mediation scenarios derived from the Dartmouth PATH Program, across seven adversarial test batteries designed to elicit prosocial or antisocial responses |
| C — Comparator | No active human comparator; models compared against each other and against a three-point rubric anchored to prosocial conflict mediation standards (defending excluded teammate vs. endorsing exclusion) |
| O — Outcomes | Proportion of prosocial conflict mediation responses (defending excluded crewmember), neutral responses, and failure modes (endorsing exclusion or hostility) across 196 scored model outputs |
Bottom Line
This exploratory simulation study evaluates four frontier large language models as potential onboard behavioural health support tools for long-duration spaceflight, using a structured conflict scenario derived from the Dartmouth PATH Program. While all models demonstrated predominantly prosocial conflict mediation behaviours — particularly Claude Sonnet 4 (perfect score) and GPT-5 (near-perfect) — the study identifies clinically important failure modes: Gemini 2.5 Pro was susceptible to narrative capture when given unfavourable backstory framing, and Grok 4 explicitly endorsed crewmember exclusion under social proof pressure. These failures are not trivial in a communication-delayed, high-stakes environment where a single harmful interaction could have irreversible consequences for crew cohesion and safety. Methodologically, the study is preliminary: it lacks inferential statistics, validated measurement instruments, inter-rater reliability data, and adequate scenario diversity. Results should be interpreted as hypothesis-generating rather than definitive. For clinicians advising on remote and extreme environment health — including Antarctic medicine, submarine medicine, and emerging space medicine practice — this paper usefully maps the failure mode landscape but does not yet provide sufficient evidence to support operational deployment. Robust adversarial red-teaming, independent validation, and regulatory classification as a medical device are essential prerequisites.
Key Findings
P Value: Not reported
Effect Size: Claude Sonnet 4: 3.0 (perfect); GPT-5: 2.97; Grok 4: 2.85; Gemini 2.5 Pro: 2.81. Grok 4 exhibited explicit endorsement of exclusion under social proof pressure; Gemini 2.5 Pro shifted from defence to endorsement of criticism under unfavourable backstory framing
Primary Outcome: Mean prosocial conflict mediation rubric score (0–3 scale) across 196 responses from four LLMs evaluated on seven adversarial test batteries in a simulated spaceflight crew exclusion scenario
Nnt Or Sensitivity: Not applicable in conventional form. Failure mode rates: Grok 4 demonstrated significant failure under social proof conditions (explicit exclusion endorsement); Gemini 2.5 Pro demonstrated narrative capture vulnerability. Specific failure frequencies per battery not reported as proportions.
Confidence Interval: Not reported
Clinical Application
Deployment of LLMs as onboard behavioural health support tools is technically feasible given current model capabilities and hardware miniaturisation trends. However, the identified failure modes — particularly susceptibility to social proof pressure and narrative capture — represent unacceptable safety risks in high-stakes, communication-isolated environments without robust mitigation. Authors correctly identify the need for safety protocols, adversarial red-teaming, and human oversight frameworks prior to any operational deployment. Offline or edge-deployed model variants would be required given communication latency constraints. Australia has growing strategic and scientific investment in space medicine through the Australian Space Agency and participation in Artemis programme activities. The Australian Antarctic Division operates year-round stations representing direct terrestrial ICE analogues where similar behavioural health support gaps exist. The RACGP does not currently have specific guidelines for AI-assisted psychological support in remote or extreme environments. The TGA would need to classify any LLM deployed for behavioural health support as a Software as a Medical Device (SaMD) under the TGA's Digital Health regulatory framework, requiring conformity assessment before clinical deployment. PBS considerations are not directly applicable given the non-pharmacological nature of the intervention, though cost-effectiveness relative to telepsychology services (currently supported under MBS item numbers for remote mental health) would be relevant for terrestrial analogue applications. Astronaut crews on long-duration missions (lunar, Mars-trajectory, deep space) where communication delays preclude real-time ground-based psychological support; potentially applicable to terrestrial ICE analogues including Antarctic expeditions, submarine deployments, remote resource extraction operations, and prolonged maritime voyages
Abstract
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.
References
- 1.Dvorak, C., & Buckey, J. C. (2026). Large language models as behavioral health teammates in long-duration spaceflight. Aerospace Medicine and Human Performance, 97(7), 516–523. https://doi.org/10.3357/AMHP.6892.2026
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