Artificial Intelligence (AI), especially Large Language Models, are reshaping educational practices, prompting renewed attention to the guardrails needed for their responsible use. As these systems become increasingly integrated into instructional workflows, educators are faced with opportunities and complexities. Understanding how instructors may engage with AI during lesson planning is therefore essential for identifying best practices and potential drawbacks, and how to benefit students and instructors alike. This project explores how AI assists instructors in lesson planning and delivery. Specifically, five instructors used a standardized prompt to ask AI to generate a detailed session plan based on class materials uploaded by the instructor. The prompt explicitly instructed AI to design sessions following the Five Characteristics of Effective Teachers: Clarity, Variability, Enthusiasm, Student Opportunity to Learn Criterion Material, and Task-Oriented and Businesslike Behaviors. Following each AI-planned session, students (n=70) completed a brief survey evaluating lecture quality across these five effectiveness categories. Instructors (n=3) also completed a self-evaluation survey addressing several dimensions, including time and effort invested in lecture preparation and overall satisfaction compared to sessions prepared without AI assistance. This project involved multiple disciplines, including Agricultural Education, Plant Nutrition, Statistical Analysis, and Meat Science. The survey results indicated  students felt the lectures provided a high rating based on the characteristics of Clarity (8.7 out of 10), Variability (8.5), Enthusiasm (8.7), Objective Based (8.9), and Task Oriented (9.0).Instructors had a wider range of responses, suggesting AI worked well at organizing and developing lecture materials; however, could take additional time to develop new material, and must be reviewed thoroughly, specifically for higher level technical lectures. Overall, our work indicates that AI can provide meaningful efficiencies in lesson planning and improve student sentiment toward course materials, but its application to technical content requires careful oversight and robust review processes for more technical topics.