Turning Language Model Training from Black Box into a Sandbox

• URL: http://arxiv.org/abs/2601.21631v1
• Date: Thu, 29 Jan 2026 12:30:55 GMT
• Title: Turning Language Model Training from Black Box into a Sandbox
• Authors: Nicolas Pope, Matti Tedre,
• Abstract summary: Browser-based tool allows students to train a small transformer language model entirely on their own device.<n>In a CS1 course, 162 students completed pre- and post-test explanations of why language models sometimes produce incorrect or strange output.
• Score: 2.8821062918162146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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