Abstract
Learners with Autism Spectrum Condition (ASC) are often characterized as having deficits in core social competencies affecting ability to communicate and interact with other people across multiple contexts. However, such deficit views of ASC lead to narrow interventions that cannot be generalized to wider learning contexts nor the real world. Taking a non-deficit view can offer a means of broadening understanding of ASC by taking a more holistic view of an individual. Thoughtfully designed VR solutions that take a non-deficit view can provide learning tools allow learners to develop their self-determination awareness and skills such as decision-making, self-advocacy, reflective problem-solving. Furthermore, through the use of machine learning approaches such solutions have the potential to provide real-time monitoring of learners during interaction offering a holistic understanding of individuals with ASC. This position paper sets out the support case for utilizing VR through non-deficit models and examines the role machine learning can play in further understanding of learners with ASC. It is intended that this sets out a case for future research directions.
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Hughes-Roberts, T., Cui, V., Mahmud, M., Brown, D.J. (2022). Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. User and Context Diversity. HCII 2022. Lecture Notes in Computer Science, vol 13309. Springer, Cham. https://doi.org/10.1007/978-3-031-05039-8_16
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