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 |
| 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 |
| 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. |
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| ISSN: | 1560-4292 1560-4306 |
| DOI: | 10.1007/s40593-023-00388-5 |
