EDBooks: AI-Enhanced Interactive Narratives for Programming Education
Fuente:
arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910700875546624 |
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| author | Oney, Steve Shen, Yue Wu, Fei Hong, Young Suh Wang, Ziang Khajekar, Yamini Zhang, Jiacheng Wang, April Yi |
| author_facet | Oney, Steve Shen, Yue Wu, Fei Hong, Young Suh Wang, Ziang Khajekar, Yamini Zhang, Jiacheng Wang, April Yi |
| contents | Large Language Models (LLMs) have shown the potential to be valuable teaching tools, with the potential of giving every student a personalized tutor. However, one challenge with using LLMs to learn new concepts is that when learning a topic in an unfamiliar domain, it can be difficult to know what questions to ask. Further, language models do not always encourage "active learning" where students can test and assess their understanding. In this paper, we propose ways to combine large language models with "traditional" learning materials (like e-books) to give readers the benefits of working with LLMs (the ability to ask personally interesting questions and receive personalized answers) with the benefits of a traditional e-book (having a structure and content that is pedagogically sound). This work shows one way that LLMs have the potential to improve learning materials and make personalized programming education more accessible to a broader audience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10687 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EDBooks: AI-Enhanced Interactive Narratives for Programming Education Oney, Steve Shen, Yue Wu, Fei Hong, Young Suh Wang, Ziang Khajekar, Yamini Zhang, Jiacheng Wang, April Yi Human-Computer Interaction Large Language Models (LLMs) have shown the potential to be valuable teaching tools, with the potential of giving every student a personalized tutor. However, one challenge with using LLMs to learn new concepts is that when learning a topic in an unfamiliar domain, it can be difficult to know what questions to ask. Further, language models do not always encourage "active learning" where students can test and assess their understanding. In this paper, we propose ways to combine large language models with "traditional" learning materials (like e-books) to give readers the benefits of working with LLMs (the ability to ask personally interesting questions and receive personalized answers) with the benefits of a traditional e-book (having a structure and content that is pedagogically sound). This work shows one way that LLMs have the potential to improve learning materials and make personalized programming education more accessible to a broader audience. |
| title | EDBooks: AI-Enhanced Interactive Narratives for Programming Education |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.10687 |