Social Robots for Pedagogical Rehabilitation: Trends and Novel Modeling Principles

Social Robots for Pedagogical Rehabilitation: Trends and Novel Modeling Principles

Vassilis G. Kaburlasos, Eleni Vrochidou
Copyright: © 2019 |Pages: 21
DOI: 10.4018/978-1-5225-7879-6.ch001
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The use of robots as educational learning tools is quite extensive worldwide, yet it is rather limited in special education. In particular, the use of robots in the field of special education is under skepticism since robots are frequently believed to be expensive with limited capacity. The latter may change with the advent of social robots, which can be used in special education as affordable tools for delivering sophisticated stimuli to children with learning difficulties also due to preexisting conditions. Pilot studies occasionally demonstrate the effectiveness of social robots in specific domains. This chapter overviews the engagement of social robots in special education including the authors' preliminary work in this field; moreover, it discusses their proposal for potential future extensions involving more autonomous (i.e., intelligent) social robots as well as feedback from human brain signals.
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A percentage around 4% of the students in member countries of the European Union (EU) are registered in special education programs according to Special Needs Education (2012) European data. At least 10% has been reported in the USA regarding children characterized by a learning difficulty (Cortiella, & Horowitz, 2014), while in Finland a reported 17% of students are enrolled in special education (Meijer, Soriano, & Watkins, 2003). Special scientists such as educators, pedagogues, psychologists and speech therapists suggest that the percentage of children in need for special education is higher than reported, since many cases are not recorded for various reasons (Pastor, & Reuben, 2008). Furthermore, if we also consider the families of children then the percentage of people involved in special education is even higher. For the aforementioned reasons, the support of children with Special Education Needs (SEN) is included in national /European /world policies (UNESCO, 1994). Children with SEN are experiencing a variety of difficulties in family as well as at school. Effective special education at an early stage may improve the emotional and social development of children with SEN, their learning capacity, and, finally, improve the quality of life for a significant part of the population. Furthermore, special education may also improve the work skills of people with SEN thus enhancing a nation’s workforce. There is a need for a policy framework regarding SEN. The latter has been a subject of debate in particular regarding whether special education itself is a problem of, or the solution to, issues of social justice (Norwich, 2007).

During the last decades robots seem to leave the industrial manufacturing floor and enter other domains such as farming, surveillance, entertainment, education, etc. Educational robotics are used worldwide as learning tools (Miller, Church, & Trexler, 2000) but surprisingly rarely in special education. At the moment, the demand for special education services remains high, yet unsatisfied due to the high cost involved. However, the benefits surpass all costs. Lately, Cyber-Physical Systems (CPSs), including social robots, have been proposed in education with emphasis on special education (CybSPEED, 2017). Note that the concept of CPSs has been introduced to account for technical devices with both sensing and reasoning abilities including a varying degree of autonomous behavior. There are a lot of expectations from CPSs (Serpanos, 2018). Seven types of CPSs are most often discussed, focusing on Disabled People, Healthcare, Agriculture and Food Supply, Manufacturing, Energy and Critical Infrastructures, Transport and Logistics, and Community Security and Safety. To them one additional type has been proposed lately, namely Education & Pedagogical Rehabilitation (CybSPEED, 2017). The CPSs we are interested in here include Social Robots in (special) education such as NAO, Pepper, Jibo, Leka etc. (Papakostas et al., 2018; Ueyama, 2015). In particular, humanoid robots such as NAO are already employed in various contexts for the treatment of children with Autism Spectrum Disorder (ASD) (Amanatiadis et al., 2017; Kaburlasos et al., 2018 January; Lytridis et al., 2018; Ueyama, 2015).

Key Terms in this Chapter

Social Robots: Is a robot that interacts and communicates with humans by following social behaviors and rules attached to its role.

Educational Robotics: Robots provided to facilitate student’s development of knowledge, skills, and attitudes.

Human-Robot Interaction: Is the study of interaction between humans as a multidisciplinary field with contributions from human-computer interaction, artificial intelligence, design and social sciences.

Robot Autonomy: The ability of a robot to possess the necessary computational resources when functioning, in terms of hardware and software, so as to be physically embedded in the environment.

SEN: Special education needs refer to people who have learning difficulties or disabilities that makes it harder for them to learn than most people of the same age, which calls for special educational provision.

Autism: An early childhood mental condition, characterized by difficulty in communication, in forming relations with others and in using language and abstract concepts.

STE: Special treatment and education is defined as the treatment and education of students with special educational needs in a way that addresses their individual differences.

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