Neutrosophic Clustering in Adaptive AI Learning for Students with Down Syndrome
Addressing various error patterns and maintaining student engagement are challenges in the education of students with Down syndrome. The complexity and unpredictability of individual learning behaviors are often beyond the capacity of existing adaptive AI frameworks. This short communication introd...
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| Format: | Article |
| Language: | English |
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MO.RI Publishing
2025-04-01
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| Series: | Journal of Digital Learning and Education |
| Subjects: | |
| Online Access: | https://journal.moripublishing.com/index.php/jdle/article/view/1539 |
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| Summary: | Addressing various error patterns and maintaining student engagement are challenges in the education of students with Down syndrome. The complexity and unpredictability of individual learning behaviors are often beyond the capacity of existing adaptive AI frameworks. This short communication introduces an innovative conceptual framework that integrates neutrosophic error clustering into adaptive AI systems, utilizing neutrosophic logic to define membership functions for truth, indeterminacy, and falsity. The framework enables real-time personalization and modifications to learning by grouping errors into conceptual, procedural, or attention-related clusters. The integration of neutrosophic with adaptive AI enhances emotional engagement, improves learning outcomes, and promotes inclusivity for students with Down syndrome. Despite being theoretical, this study establishes a framework for additional research and real-world applications.
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| ISSN: | 2798-1088 2776-4060 |