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About

Helen Hastie is a prominent researcher whose work sits at the intersection of human-robot interaction (HRI), natural language processing, and artificial intelligence. Her scholarship spans robotic tutoring systems, transparency in AI, trust calibration, and autonomous systems for high-stakes industrial environments, making her a versatile and influential figure in human-centered robotics research. Hastie has made significant contributions to understanding how robots can serve as empathic, personalized tutors — her 2014 interview study on empathic robotic tutors in classrooms (71 citations) helped shape early conversations around educational robotics in Europe. Her work on the ORCA Hub, an EPSRC-funded offshore robotics project, demonstrated real-world applications of autonomous systems and digital twins in hazardous energy environments, garnering substantial attention (56 citations). Perhaps most impactful is her sustained focus on trust and transparency: her 2021 paper on robot errors and decision-making (65 citations) and subsequent investigations into Theory of Mind, robot personality, and propensity-to-trust scales collectively illuminate how humans and robots build reliable partnerships. With over 350 cumulative citations across her key works, Hastie's research meaningfully advances our understanding of how robots can be designed to interact safely, transparently, and empathically within human societies.