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AI Adaptivity in a Mixed-Reality System Improves Learning

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Title: AI Adaptivity in a Mixed-Reality System Improves Learning
Language: English
Authors: Nesra Yannier (ORCID 0000-0002-3660-1903), Scott E. Hudson, Henry Chang, Kenneth R. Koedinger
Source: International Journal of Artificial Intelligence in Education. 2024 34(4):1541-1558.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 18
Publication Date: 2024
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 2005966
Document Type: Journal Articles
Reports - Research
Descriptors: Artificial Intelligence, Computer Software, Feedback (Response), Outcomes of Education, Intelligent Tutoring Systems, Algorithms, Student Characteristics, Science Instruction, Learning Analytics, Bayesian Statistics, Inquiry, Active Learning
DOI: 10.1007/s40593-023-00388-5
ISSN: 1560-4292
1560-4306
Abstract: Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children's inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children's science learning.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1453605
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Nesra+Yannier%22">Nesra Yannier</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-3660-1903">0000-0002-3660-1903</externalLink>)<br /><searchLink fieldCode="AR" term="%22Scott+E%2E+Hudson%22">Scott E. Hudson</searchLink><br /><searchLink fieldCode="AR" term="%22Henry+Chang%22">Henry Chang</searchLink><br /><searchLink fieldCode="AR" term="%22Kenneth+R%2E+Koedinger%22">Kenneth R. Koedinger</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22International+Journal+of+Artificial+Intelligence+in+Education%22"><i>International Journal of Artificial Intelligence in Education</i></searchLink>. 2024 34(4):1541-1558.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children's inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children's science learning.
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