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的日常生活，K-12阶段AI教育面临的主要挑战之一是设计能有机融入现有学科教学的深度学习体验。本文介绍了一项与AI4K12标准对齐的课程设计与实施，该课程将AI学习目标植入农村初中科学课堂，采用广度优先搜索（BFS）作为理解AI问题解决的入门路径。通过无插电活动和交互式模拟环境，学生将BFS学习为探索网络与识别最短路径的策略，进而将其应用于涉及病毒传播与接触追踪的科学情境。为评估学习效果与参与度，我们分析了前后测评估、学生作品及教师访谈记录。结果表明：学生能有效参与课程，对BFS和AI问题解决的理解显著提升，并在现有科学教学中获益。教师反馈进一步表明该模块既自然融入科学课程体系，又支撑了预期的科学教学目标。最后，我们提出关于扩大AI问题解决教育普惠性的课程与设计建议。