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Managing social-educational robotics for students with autism spectrum disorder through business model canvas and customer discovery

Authors :
Anshu Saxena Arora
Amit Arora
K. Sivakumar
John R. McIntyre
Source :
Frontiers in Robotics and AI, Vol 11 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students. This research aims to (a) obtain new scientific knowledge on social-educational robotics by exploring the usage of social robots (especially humanoids) and robotic interventions with ASD students at high schools through an ASD student–teacher co-working with social robot–social robotic interactions triad framework; (b) utilize Business Model Canvas (BMC) methodology for robot design and curriculum development targeted at ASD students; and (c) connect interdisciplinary areas of consumer behavior research, social robotics, and human-robot interaction using customer discovery interviews for bridging the gap between academic research on social robotics on the one hand, and industry development and customers on the other. The customer discovery process in this research results in eight core research propositions delineating the contexts that enable a higher quality learning environment corresponding with ASD students’ learning requirements through the use of social robots and preparing them for future learning and workforce environments.

Details

Language :
English
ISSN :
22969144 and 46213708
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Robotics and AI
Publication Type :
Academic Journal
Accession number :
edsdoj.69520dee26664e11b46213708b141d17
Document Type :
article
Full Text :
https://doi.org/10.3389/frobt.2024.1328467