On the robustness of ChatGPT in teaching Korean Mathematics
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866929718243098624 |
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| author | Nguyen, Phuong-Nam Nguyen-The, Quang Vu-Minh, An Nguyen, Diep-Anh Pham, Xuan-Lam |
| author_facet | Nguyen, Phuong-Nam Nguyen-The, Quang Vu-Minh, An Nguyen, Diep-Anh Pham, Xuan-Lam |
| contents | ChatGPT, an Artificial Intelligence model, has the potential to revolutionize education. However, its effectiveness in solving non-English questions remains uncertain. This study evaluates ChatGPT's robustness using 586 Korean mathematics questions. ChatGPT achieves 66.72% accuracy, correctly answering 391 out of 586 questions. We also assess its ability to rate mathematics questions based on eleven criteria and perform a topic analysis. Our findings show that ChatGPT's ratings align with educational theory and test-taker perspectives. While ChatGPT performs well in question classification, it struggles with non-English contexts, highlighting areas for improvement. Future research should address linguistic biases and enhance accuracy across diverse languages. Domain-specific optimizations and multilingual training could improve ChatGPT's role in personalized education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the robustness of ChatGPT in teaching Korean Mathematics Nguyen, Phuong-Nam Nguyen-The, Quang Vu-Minh, An Nguyen, Diep-Anh Pham, Xuan-Lam Artificial Intelligence History and Overview I.2.7; K.3.1; G.3 ChatGPT, an Artificial Intelligence model, has the potential to revolutionize education. However, its effectiveness in solving non-English questions remains uncertain. This study evaluates ChatGPT's robustness using 586 Korean mathematics questions. ChatGPT achieves 66.72% accuracy, correctly answering 391 out of 586 questions. We also assess its ability to rate mathematics questions based on eleven criteria and perform a topic analysis. Our findings show that ChatGPT's ratings align with educational theory and test-taker perspectives. While ChatGPT performs well in question classification, it struggles with non-English contexts, highlighting areas for improvement. Future research should address linguistic biases and enhance accuracy across diverse languages. Domain-specific optimizations and multilingual training could improve ChatGPT's role in personalized education. |
| title | On the robustness of ChatGPT in teaching Korean Mathematics |
| topic | Artificial Intelligence History and Overview I.2.7; K.3.1; G.3 |
| url | https://arxiv.org/abs/2502.11915 |