Distinguishing performance gains from learning when using generative AI
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866918500088414208 |
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| author | Yan, Lixiang Greiff, Samuel Lodge, Jason M. Gašević, Dragan |
| author_facet | Yan, Lixiang Greiff, Samuel Lodge, Jason M. Gašević, Dragan |
| contents | Generative artificial intelligence (AI) is increasingly being integrated into education, where it can boost learners' performance. However, these uses do not promote the deep cognitive and metacognitive processing that are required for high-quality learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13731 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distinguishing performance gains from learning when using generative AI Yan, Lixiang Greiff, Samuel Lodge, Jason M. Gašević, Dragan Machine Learning Human-Computer Interaction Generative artificial intelligence (AI) is increasingly being integrated into education, where it can boost learners' performance. However, these uses do not promote the deep cognitive and metacognitive processing that are required for high-quality learning. |
| title | Distinguishing performance gains from learning when using generative AI |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2605.13731 |