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About

Stefan Sonderegger is a researcher specializing in the integration of social robots and artificial intelligence into higher education, with a particular focus on human-robot interaction, technology acceptance, and pedagogical innovation. His work sits at the intersection of educational technology, robotics, and learning sciences, exploring how intelligent systems can meaningfully augment teaching and learning processes. Sonderegger's most influential contribution is his 2020 study evaluating student acceptance of the humanoid robot Pepper within a university academic writing course, applying the UTAUT framework to understand how higher education students engage with social robots — a paper that has garnered 99 citations and become a key reference in the field. Building on this foundation, he has developed conceptual frameworks for designing social robot use cases in academic settings, investigated how robots can enhance teaching quality, and explored the technical integration of robots with learning management systems. More recently, Sonderegger has turned his attention to generative language models as a means of overcoming the conversational limitations of current social robots, positioning his research at a timely frontier. Through empirical studies, conceptual overviews, and multilingual publications, his body of work has established him as a thoughtful and productive voice shaping the future of robot-assisted education.