Long-duration space exploration will place small, interdependent teams in isolated, confined, and extreme (ICE) environments, where they will face risks including communication delays, high autonomy, increased workload, team tension, and intense stress. All of these risks require research studies aimed at mitigating them, many of which take place at analog space stations. However, these analog missions, which aim to study human behavior in ICE settings, suffer from chronic survey noncompliance, which threatens data integrity and reduces the accuracy of crew monitoring. This project examines which individual characteristics predict who completes research surveys during missions at the Mars Desert Research Station, a Mars-analog habitat in Utah. Before their missions, crew members completed questionnaires assessing personality (with a focus on conscientiousness), their identification with their team, their cultural values (especially collectivism, or putting the group before oneself), and their difficulties with emotion regulation. During the mission, they were asked to complete brief daily and weekly surveys. For each person, I will calculate survey compliance as the percentage of assigned surveys completed, and I will use regression models to test whether conscientiousness, emotion regulation, team identification, and collectivism predict higher compliance. I will also test whether collectivism strengthens the link between team identification and survey completion, such that highly collectivistic crew members who strongly identify with their team are especially likely to respond. I expect that crew members who are more conscientious, better at managing their emotions, more strongly identified with their team, and more collectivistic will show higher survey compliance. The work integrates literature on ICE stressors, organizational citizenship behavior (OCB), survey methodology, and individual differences to generate actionable recommendations for designing analog-station research protocols and for operational monitoring in deep-space missions.

Hi, my name is Amber Bartlett. I’m an honors psychology student at George Mason University. My project is called “When No Response Is a Response, predicting survey non-compliance in isolated, confined, extreme, analog space missions”

My mentor is Dr. Lauren Kuykendall in the Industrial Organizational Psychology Department. So what is the big picture of my project? Long duration missions to Mars and to the moon will place very small crews into what we called isolated, confined, extreme, or ICE environments. These missions will involve tight living quarters, high workloads, autonomy, communication delays, and one of the tools we can use to track how these people are doing is through self-report surveys.

But the issue with doing research on ICE teams is the fact that the sample sizes are incredibly small, so when people skip surveys, we lose statistical power and increase the risk of error in our findings, and missingness is often not random. The people who stop answering might be the ones who are most stressed out, which means that things can be biased. So this leads to my core question, can we predict who complies with surveys before a mission? If we can identify likely non-responders ahead of time, then we can actually anticipate, model, and design around that.

So this project is a part of a collaboration between the University of Central Florida, where we’ll be using UCF’s IO Psychology PhD student Andres Käosaar’s data set from the Mars Desert Research Station. So MDRS is a Mars analog habitat where small crews simulate living on Mars. They’ll be conducting EVAs, conserving resources, and living in a confined environment.

And for this project, I will be using the data set that includes 99 individuals, 16 crew members, and a total of 761 habitat days. Most of the missions had teams of about six to eight members, and participation in Andres’ study was completely voluntary. Now because it was voluntary, I treat filling out these surveys as a type of organizational censorship behavior, and this is when people are doing small extra things at work that help their team organization, because doing his research survey was completely just to help his research.

So to understand why some people keep doing this extra work and why others don’t, I focus on four key constructs from the literature. So that’s including conscientiousness, emotional regulation difficulties, team identification, and collectivism within someone’s culture. So the knowledge gap is that analog space research already acknowledges chronic survey non-compliance and missingness.

However, individual level predictors or survey compliance and ICE teams remains undermodeled. We don’t have a clear guidance on what type of people are most likely to disappear from the data set, so my project is designed to help close that gap. So within this data set, participants completed pre-mission surveys which included measures such as conscientiousness, emotional regulation difficulties, team identification, and collectivism.

So during the mission, they received daily and weekly self-report surveys about their experience and well-being, and my main outcome is survey compliance, so this is defined as the percentage of assigned surveys each person completes over the mission. So based on this, I have five different hypotheses. First is higher conscientiousness will predict greater survey compliance.

Second, better emotional regulation will predict greater survey compliance. Stronger collectivism, stronger team identification, and collectivism moderates team ID compliance. All of this, I’m going to assume it will increase greater survey compliance.

Now with OSCAR’s help, I was able to visit MDRS in person, and these photos here show the habitat. It shows the people that I was with out there, and it really helped me understand what it’s like to be living in one of these space analog stations. So my next step is to run descriptive analysis on all of the hypotheses that I just showed you, and so that will be what I’m doing next semester.

The expected implications is that if my hypotheses are supported, this work suggests that voluntary survey compliance in ICE analogs is not random. It’s a predictable citizenship-like behavior rooted in personality, emotional regulation, and how people relate to their teams and groups. So this has several implications such as pre-mission screening, low burden monitoring, and then just identifying patterns of non-response, but also if my hypotheses are not supported, it means we should be looking somewhere else other than the OCB literature, which right now is where the main source of research is coming from when it comes to non-response and surveys.

So I want to thank my mentor, Dr. Lauren Kuykendall, Andres Käosaar, Dr. Seth Kaplan, Dr. Brielmaier, my psychology honors cohort, the MDRS director, and of course OSCAR URSP. If you would like a link to all of my resources that I’ve used, here’s a QR code for that. Thank you so much for listening.

Artificial intelligence (AI) has rapidly evolved from a technological innovation into a central force reshaping global geopolitics. As states race to harness AI’s economic and strategic potential, the technology has become a defining factor in international competition, cooperation, and governance. This paper examines how AI is transforming global power dynamics by shifting influence toward actors with advanced computational capacity, vast data resources, and strong innovation ecosystems. The analysis argues that AI functions as a dual-use geopolitical lever: it intensifies strategic rivalries among major powers while simultaneously creating new opportunities for collective regulation and shared ethical standards.
AI is altering the nature of conflict by enabling autonomous weapons systems, accelerating cyber operations, and enhancing disinformation campaigns that blur traditional distinctions between war and peace. These developments challenge established security doctrines and create new vulnerabilities that demand coordinated international responses. At the same time, AI exposes significant gaps in global governance, raising questions about accountability, transparency, surveillance, and human rights that existing institutions struggle to address.
Using a qualitative synthesis of contemporary scholarship, the paper maps AI’s geopolitical effects across four domains: great-power competition, military and security transformation, global governance architectures, and regional ethical perspectives. The findings show that while AI amplifies inequalities and fuels geopolitical tension, it also offers pathways for new regulatory frameworks and multilateral cooperation.
Ultimately, the paper contends that the geopolitical future of AI will depend not on technological inevitability but on the ethical, political, and institutional choices made by human actors. Ensuring that AI advances global stability and human well-being will require governance models that balance innovation, security, and shared responsibility.

Hello,
In the early twenty-first century, artificial intelligence has emerged not simply as a technological breakthrough but as a transformative geopolitical force—one that is redefining how power is created, exercised, and contested on the global stage. What once belonged to research labs and commercial enterprises has now become a central pillar of national strategy. Major powers—the United States, China, and the European Union—treat AI with the same seriousness that past generations reserved for nuclear technology. It’s entirely accurate to say that AI has emerged as a powerful and influential tool in shaping global political dynamics.
This transformation raises a key question: How does AI reshape global geopolitics by altering power relations, national security, and international governance? The answer, as this research argues, lies in understanding AI as a dual-use geopolitical lever. On one hand, AI fuels strategic rivalries, widening technological inequalities, and destabilizing traditional hierarchies. On the other, it creates unprecedented opportunities for shared regulation, ethical standards, and cooperative global frameworks.
AI is redistributing power in three critical ways.
First, it rewards states and corporations with data, computational capacity, and technological expertise—not those with traditional industrial strength. The result is a new form of digital stratification that determines who leads, who follows, and who risks being left behind.
Second, AI is redefining conflict itself. Autonomous weapons systems, cyberattacks powered by machine learning, and sophisticated disinformation campaigns blur the boundaries between war and peace. Deterrence, defense, and security doctrines are being rewritten in real time.
Third, AI challenges the foundations of global governance. It raises profound questions about accountability, transparency, and ethics—questions no nation can resolve alone. International law, built for an earlier technological era, must now confront algorithms that make decisions far faster than traditional institutions can respond.
But this story is not only about rivalry. It is also about responsibility. AI forces us to reconsider what human agency means in an age of automated decision-making. It compels us to think about who benefits from technology and who might be harmed by it. And it reminds us that the future of global stability will depend not on machines themselves, but on human choices—ethical, political, and institutional.
If nations view AI only as a weapon of competition, we risk repeating the mistakes of past arms races. But if we recognize its dual-use nature—its capacity to inspire both competition and cooperation—we can harness AI to strengthen global governance, promote shared security, and advance human flourishing.
In the end, the geopolitical future of artificial intelligence will be determined not by the power of our algorithms, but by the wisdom of our decisions. Let us choose a future where AI serves humanity—rather than one where humanity becomes subordinate to its own creations.
Thank you for your attention.