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About

Mariah Schrum is a researcher whose work sits at the intersection of human-robot interaction (HRI), robot learning, and explainable AI. She is perhaps best known for her rigorous methodological contributions to HRI research, having authored influential work critiquing and reforming the use of Likert scales in the field — work that has collectively accumulated over 120 citations and prompted broader conversations about statistical best practices across the discipline. Her research into human trust following robot mistakes (29 citations) and the effects of robot skill level and communication during close collaboration (16 citations) reflects a deep commitment to understanding how humans and robots can work together safely and effectively. Schrum has also explored socially impactful applications, including a humanoid therapy robot designed to encourage exercise in dementia patients (27 citations) and a novel mosquito pick-and-place system for malaria vaccine production. Her work on personalized robot learning — particularly the MIND MELD framework for imitation learning (23 citations) — demonstrates her technical range. More recently, her research on adaptive personalized explainability highlights her growing focus on transparency and trust in AI-driven systems, making her a versatile and socially conscious voice in modern robotics research.