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Low-cost social robot designs for education: a review
Edger P Rutatola1,2, Frank C Msonge2, Remko Proesmans1
1IDLab-AIRO, Ghent University-imec, Ghent, Belgium.
None:
Social robots have shown promising potential in educational contexts worldwide, with studies reporting significant cognitive and affective gains when such robots are deployed. However, among other factors, the high cost of commercial robots limits this line of research to a small number of laboratories and hinders large-scale adoption in real-world educational settings, with most studies remaining short-term pilot interventions. Although several reviews exist in this domain, they primarily focus on applications, impacts, and trends, with limited attention to robot design in relation to affordability. Using a systematic literature review, this study identifies the features and capabilities commonly incorporated in low-cost social robots in educational contexts, as well as the design choices that balance effectiveness and affordability. Thirty-four (34) studies describing low-cost custom social robot designs were analysed following PRISMA guidelines. Articles were retrieved from Web of Science, Scopus, IEEE Xplore, and the ACM Digital Library, and analysed using content analysis as well as descriptive and inferential statistics. The findings show that speech-based interaction (73.5%), vision capabilities (53%), and body-part movement (47%) are the most frequently reported features supporting learner-robot interaction. Additional features include expressive displays, touch input, and mobility. While 3D-printed housings are the most commonly adopted solution, alternative low-cost materials such as plywood and plush toys are also used. The use of smartphones as integrated computational and interaction platforms emerged as an innovative approach. Furthermore, commonly used sensors, actuators, and compute platforms are identified. This review provides a design-oriented perspective on developing effective yet affordable social robots and highlights practical design trade-offs for researchers, particularly in low-resource contexts.
