Democratizing Foundations of Problem-Solving with AI: A Breadth-First Search Curriculum for Middle School Students
Griffin Pitts, Kimia Fazeli, Tirth Bhatt, Jennifer Albert, Marnie Hill, Tiffany Barnes, Shiyan Jiang, Bita Akram

TL;DR
This paper introduces an AI curriculum for middle school science classes that uses Breadth-First Search to teach AI problem-solving, demonstrating positive student engagement and learning outcomes.
Contribution
It presents a novel, integrated curriculum embedding BFS-based AI problem-solving within science instruction for middle school students.
Findings
Students improved understanding of BFS and AI problem-solving.
Students engaged productively with unplugged activities and simulations.
Teacher feedback indicated good curriculum fit and support for science learning.
Abstract
As AI becomes more common in students' everyday experiences, a major challenge for K-12 AI education is designing learning experiences that can be meaningfully integrated into existing subject-area instruction. This paper presents the design and implementation of an AI4K12-aligned curriculum that embeds AI learning goals within a rural middle school science classroom using Breadth-First Search (BFS) as an accessible entry point to AI problem-solving. Through unplugged activities and an interactive simulation environment, students learned BFS as a strategy for exploring networks and identifying shortest paths, then applied it to science contexts involving virus spread and contact tracing. To examine engagement and learning, we analyzed pre- and post-assessments, student work artifacts, and a teacher interview. Results suggest that students engaged productively with the curriculum,…
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