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.

翻译：随着人工智能日益融入学生日常体验，K-12 AI教育面临的一大挑战是如何设计能有机融入现有学科教学的课程。本文介绍了一套符合AI4K12标准的课程设计与实施，该课程以广度优先搜索（BFS）作为AI问题解决的入门切入点，并将其嵌入农村初中科学课堂。通过无插电活动和交互式模拟环境，学生将BFS作为探索网络和识别最短路径的策略进行学习，随后将其应用于涉及病毒传播和接触者追踪的科学情境中。为衡量参与度和学习成效，我们分析了前后测评估、学生作品以及教师访谈。结果表明，学生能够积极参与课程，对BFS和AI问题解决的理解有所提升，并在现有科学教学中受益于这些概念的学习。教师反馈进一步指出，该模块能很好地融入科学课程，同时支持预期的科学学习目标。最后，我们总结了在扩大AI问题解决学习普及范围方面的课程与设计考量。