Motivation As generative AI tools become ubiquitous in student work, engineering educators need evidence-based guidance on integration strategies. This study examines whether structured AI exposure enhances learning in foundational engineering coursework. Project Description This project investigates whether structured exposure to generative AI can enhance student learning and problem-solving abilities in Engineering Mechanics 1 (EK301) compared to traditional instruction alone. The innovation involves embedding short, guided demonstrations of AI tools such as Gemini and ChatGPT directly into weekly lectures to model effective prompting, solution verification, and critical evaluation. Using a quasi-experimental design across three EK301 sections at Boston University in Spring 2026, one section will receive the AI-enhanced instruction while two serve as controls. All students may use AI for assignments but not for quizzes or exams (consistent with the long standing course policy on use of external resources). Surveys, course performance data, and instructor observations will provide both quantitative and qualitative evidence on how structured AI integration influences learning. The project addresses a critical educational need for evidence-based strategies that help engineering students use AI productively and ethically while preserving core analytical skills.\\ Goals and Outcomes The primary goal of this project is to determine how structured, guided exposure to generative AI can improve student learning and problem-solving in engineering mechanics. The project will generate evidence-based insights into effective classroom integration of AI tools and inform best practices for balancing AI-assisted learning with core analytical skill development. Ultimately, the work will contribute to sustainable, data-driven improvements in teaching practices across foundational engineering courses.