Problem
Research questions and friction points this paper is trying to address.
AI education
K-12
curriculum integration
problem-solving
Breadth-First Search
Democratizing Foundations of Problem-Solving with AI: A Breadth-First Search Curriculum for Middle School Students
🤖 AI Summary
This study investigates how to effectively integrate AI education into K–12 science classrooms—particularly in rural middle schools—to advance educational equity and accessibility. Grounded in the AI4K12 framework, the project introduces breadth-first search (BFS) as an entry point to AI problem-solving, employing device-free activities and interactive simulations that situate learning within authentic scientific contexts such as virus transmission and contact tracing. This approach enables students to grasp concepts of network exploration and shortest-path algorithms through experiential engagement. By incorporating formative assessment and learning analytics, the instructional design seamlessly blends AI literacy with disciplinary science content. Findings indicate significant improvements in students’ understanding of BFS and AI problem-solving strategies, while teacher feedback confirms strong alignment with science curriculum objectives and affirms the module’s effectiveness in supporting student learning outcomes.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Breadth-First Search
AI education
K-12 integration
unplugged activities
problem-solving
🔎 Similar Papers
No similar papers found.
