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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2502.20354 |
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| _version_ | 1866913710982823936 |
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| author | Drushchak, Nazarii Tyshchenko, Vladyslava Polyakovska, Nataliya |
| author_facet | Drushchak, Nazarii Tyshchenko, Vladyslava Polyakovska, Nataliya |
| contents | The growth of Educational Technology (EdTech) has enabled highly personalized learning experiences through Artificial Intelligence (AI)-based recommendation systems tailored to each student needs. However, these systems can unintentionally introduce biases, potentially limiting fair access to learning resources. This study presents a recommendation system for K-12 students, combining graph-based modeling and matrix factorization to provide personalized suggestions for extracurricular activities, learning resources, and volunteering opportunities. To address fairness concerns, the system includes a framework to detect and reduce biases by analyzing feedback across protected student groups. This work highlights the need for continuous monitoring in educational recommendation systems to support equitable, transparent, and effective learning opportunities for all students. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20354 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study Drushchak, Nazarii Tyshchenko, Vladyslava Polyakovska, Nataliya Artificial Intelligence Computation and Language The growth of Educational Technology (EdTech) has enabled highly personalized learning experiences through Artificial Intelligence (AI)-based recommendation systems tailored to each student needs. However, these systems can unintentionally introduce biases, potentially limiting fair access to learning resources. This study presents a recommendation system for K-12 students, combining graph-based modeling and matrix factorization to provide personalized suggestions for extracurricular activities, learning resources, and volunteering opportunities. To address fairness concerns, the system includes a framework to detect and reduce biases by analyzing feedback across protected student groups. This work highlights the need for continuous monitoring in educational recommendation systems to support equitable, transparent, and effective learning opportunities for all students. |
| title | Towards Responsible AI in Education: Hybrid Recommendation System for K-12 Students Case Study |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2502.20354 |