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About

Nathan L. Tenhundfeld is a human factors and human-AI interaction researcher whose work sits at the intersection of trust, autonomous systems, and collaborative decision-making. His research addresses some of the most pressing questions in modern automation: how do humans develop, calibrate, and maintain trust in AI-driven systems, and how can these systems be designed to support effective human-machine teaming? Tenhundfeld's most cited work (16 citations) explores drone-based AI in search and rescue operations, examining how transparency and intelligent agents can improve situational awareness in high-stakes, low-visibility environments. Complementing this, his investigations into human-AI trust using autonomous ground vehicles and transparent AI systems reveal how behavioral and self-report measures converge to capture the nuanced nature of user trust. His research on virtual conflict-mediation agents (11 citations) demonstrates how socially intelligent systems can facilitate mission planning and team decision-making in military-relevant contexts. Beyond operational settings, Tenhundfeld has explored how robot anthropomorphism shapes user perception, empathy, and willingness to deploy robots across diverse career categories and high-risk environments. Collectively, his growing body of work—spanning virtual environments, autonomous vehicles, and social robotics—establishes him as an emerging voice in designing human-centered AI systems that are trustworthy, transparent, and effective in demanding real-world applications.