Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition

Thomas Hughes-Roberts; Vanessa Cui; Mufti Mahmud; David J. Brown

doi:10.1007/978-3-031-05039-8_16

Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition

Thomas Hughes-Roberts^*, Vanessa Cui, Mufti Mahmud, David J. Brown

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
Editors	Margherita Antona, Constantine Stephanidis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	231-240
Number of pages	10
ISBN (Print)	9783031050381
DOIs	https://doi.org/10.1007/978-3-031-05039-8_16
State	Published - 2022
Externally published	Yes
Event	16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022 - Virtual, Online Duration: 26 Jun 2022 → 1 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13309 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022
City	Virtual, Online
Period	26/06/22 → 1/07/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Autism
Machine learning
Virtual Reality

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-05039-8_16

Cite this

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 M. Antona, & C. Stephanidis (Eds.), Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings (pp. 231-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13309 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-05039-8_16

Hughes-Roberts, Thomas ; Cui, Vanessa ; Mahmud, Mufti et al. / Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition. Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. editor / Margherita Antona ; Constantine Stephanidis. Springer Science and Business Media Deutschland GmbH, 2022. pp. 231-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition",

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.",

keywords = "Autism, Machine learning, Virtual Reality",

author = "Thomas Hughes-Roberts and Vanessa Cui and Mufti Mahmud and Brown, \{David J.\}",

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editor = "Margherita Antona and Constantine Stephanidis",

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Hughes-Roberts, T, Cui, V, Mahmud, M & Brown, DJ 2022, Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition. in M Antona & C Stephanidis (eds), Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13309 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 231-240, 16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022, Virtual, Online, 26/06/22. https://doi.org/10.1007/978-3-031-05039-8_16

Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition. / Hughes-Roberts, Thomas; Cui, Vanessa; Mahmud, Mufti et al.
Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. ed. / Margherita Antona; Constantine Stephanidis. Springer Science and Business Media Deutschland GmbH, 2022. p. 231-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13309 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition

AU - Hughes-Roberts, Thomas

AU - Cui, Vanessa

AU - Mahmud, Mufti

AU - Brown, David J.

PY - 2022

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N2 - 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.

AB - 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 DJ. 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, editors, Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 231-240. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-05039-8_16

Leveraging Virtual Reality and Machine Learning as Mediated Learning Tools for Social Skill Development in Learners with Autism Spectrum Condition

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this