Can Non-Humanoid Social Robots Reduce Workload of Special Educators: An Online and In-Premises Field Study

Paul, N and Ramesh, S and Bhattacharya, C and Ramesh, J and Vijayan, P (2021) Can Non-Humanoid Social Robots Reduce Workload of Special Educators: An Online and In-Premises Field Study. In: 2021 IEEE International Conference on Robotics and Automation, 30 May - 5 Jun 2021, Xi'an, pp. 11386-11392.

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Abstract

Although Socially Assistive Robotics have been used in Autism Spectrum Disorder (ASD) interventions, such studies often exclude Special Educators (SEs) and often use expensive humanoid robots. In this paper, we investigate whether non-humanoid toy robots can act as teaching aids in ASD Education, in particular, can they reduce the workload of SEs. We target two most common yet divergent problems from Individualized Education Plans (IEPs) of ASD children - communication and gross motor skills. We present results from three studies a) toy robot Cozmo assists SEs in verbal lessons in school premises, b) mini drone Tello helps SEs in exercise lessons in school premises, and c) Cozmo, SEs, and ASD children connect remotely, as mandated due to the Covid-19 pandemic, for verbal lessons. All three studies showed improvement in learning outcomes and reduction in prompts from the SEs, denoting reduced workload. The effect of a robot's virtual presence in online ASD interventions has not been studied before. However, our results show that children spent more time on lessons in online intervention with Cozmo, suggesting that using robots should also be considered when designing online interventions. Furthermore, the roles of Cozmo were analyzed, and we found children showed increased spontaneous interaction when Cozmo acts as a Co-Instructor. Thus, preliminary results indicate toy robots, as opposed to expensive humanoids, may have significant potential in aiding SEs in Autism education. © 2021 IEEE

Item Type:	Conference Paper
Publication:	Proceedings - IEEE International Conference on Robotics and Automation
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	18 Mar 2022 11:59
Last Modified:	18 Mar 2022 11:59
URI:	http://eprints.iisc.ac.in/id/eprint/71606

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