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The planetary model of the atom is alive and well in middle school science class—and in popular iconography—despite most educated adults’ awareness of its shortcomings. The model persists because it is easily visualized, intuitively understandable, and expresses important truths. Models don’t have to get everything right to be useful. Middle schoolers would be overwhelmed by a more correct description of electron orbitals as probability densities satisfying the Schrödinger equation. Better to just show orbitals as ellipses. In the current era, when immensely powerful AI technologies built on neural networks are rapidly disrupting the world, K-12 students need age-appropriate models of neural networks just as they need age-appropriate models of atoms. We suggest the linear threshold unit as the best model for introducing middle school students to neural computation, and we present an interactive tool, Neuron Sandbox, that facilitates their learning.
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This content will become publicly available on April 11, 2026
Learning to Think like a Neuron in Middle School
Neuron Sandbox is a browser-based tool that helps middle school students grasp basic principles of neural computation. It simulates a linear threshold unit applied to binary decision problems, which students solve by adjusting the unit's threshold and/or weights. Although Neuron Sandbox provides extensive visualization aids, solving these problems is challenging for students who have not yet been exposed to algebra. We collected survey, video, and worksheet data from 21 seventh grade students in two sections of an AI elective, taught by the same teacher, that used Neuron Sandbox. We present a scaffolding strategy that proved effective at guiding these students to achieve mastery of these problems. While the amount of scaffolding required was more than we originally anticipated, by the end of the exercise students understood the computation that linear threshold units perform and were able to generalize their understanding of the worksheet’s solve for threshold strategy to also solve for weights.
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- PAR ID:
- 10589509
- Publisher / Repository:
- Association for the Advancement of Artificial Intelligence
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 39
- Issue:
- 28
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 29212 to 29219
- Subject(s) / Keyword(s):
- neural networks artificial intelligence middle school
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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