Democratizing Foundations of Problem-Solving with AI: A Breadth-First Search Curriculum for Middle School Students

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, improved their understanding of BFS and AI problem-solving, and benefited from learning these ideas within ongoing science instruction. Teacher feedback further indicated that the module fit well within the science curriculum while supporting intended science learning outcomes. We conclude with curriculum and design considerations for broadening access to learning about problem-solving with AI in education.