Teachers perceive distinct competency profiles in soft and hard social robots for supporting learning

The promise of social robot applications for children’s education has attracted growing enthusiasm over the past decade, with the potential to augment and support diverse learning outcomes. However, the adoption of education robots and their expected benefits for children are yet to berealised, due to complexity, cost, and variability between robots. Soft robots offer a possible solution. However, a concern is that these robots may be seen as less competent, decreasing their adoption and utility in learning environments. In this preregistered, mixed-methods study, we investigated teachers’ (n = 120) perception of 12 hard and soft social robots along different dimensions, learning tasks, roles, and contexts. Teachers perceived hard robots as more competent, human-like, and familiar than soft robots. Soft robots were perceived as more physically/visually warm. Hard robots were also more likely to be perceived as suitable for “technical tasks” and adopting a teacher/tutor role for supporting the learning of adults or groups. Soft robots were more likely to be evaluated as suitable for use with younger learners in individual learning contexts and playing the role of a co-learner/novice. This study provides a detailed account of how soft and hard robot features influence teachers’ perceptions of robot suitability for education applications. The findings directly inform how to optimise the design and situation of social robots to maximize adoption, effectiveness, and accessibility across diverse learners and learning contexts. By highlighting the nuanced trade-offs between competence and warmth, this research challenges theoretical assumptions that complex hard robots are universally superior in educational settings.