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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21631 |
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| _version_ | 1866917232155557888 |
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| author | Pope, Nicolas Tedre, Matti |
| author_facet | Pope, Nicolas Tedre, Matti |
| contents | Most classroom engagements with generative AI focus on prompting pre-trained models, leaving the role of training data and model mechanics opaque. We developed a browser-based tool that allows students to train a small transformer language model entirely on their own device, making the training process visible. In a CS1 course, 162 students completed pre- and post-test explanations of why language models sometimes produce incorrect or strange output. After a brief hands-on training activity, students' explanations shifted significantly from anthropomorphic and misconceived accounts toward data- and model-based reasoning. The results suggest that enabling learners to directly observe training can support conceptual understanding of the data-driven nature of language models and model training, even within a short intervention. For K-12 AI literacy and AI education research, the study findings suggest that enabling students to train - and not only prompt - language models can shift how they think about AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21631 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Turning Language Model Training from Black Box into a Sandbox Pope, Nicolas Tedre, Matti Computers and Society Most classroom engagements with generative AI focus on prompting pre-trained models, leaving the role of training data and model mechanics opaque. We developed a browser-based tool that allows students to train a small transformer language model entirely on their own device, making the training process visible. In a CS1 course, 162 students completed pre- and post-test explanations of why language models sometimes produce incorrect or strange output. After a brief hands-on training activity, students' explanations shifted significantly from anthropomorphic and misconceived accounts toward data- and model-based reasoning. The results suggest that enabling learners to directly observe training can support conceptual understanding of the data-driven nature of language models and model training, even within a short intervention. For K-12 AI literacy and AI education research, the study findings suggest that enabling students to train - and not only prompt - language models can shift how they think about AI. |
| title | Turning Language Model Training from Black Box into a Sandbox |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2601.21631 |