2025 ASEE Annual Conference & Exposition

A New Course on "Artificial Intelligence for Engineering Managers" - Objectives, Teaching Methods and Structure

Presented at AI in the Engineering Management Classroom

The course Artificial Intelligence for Engineering Managers has been offered four times, and this paper showcases its bold vision, pioneering teaching strategies, agile structure, and critical lessons learned through real-world delivery. As one of the first courses of its kind, it empowers engineering managers with a robust understanding of artificial intelligence (AI) and machine learning—without requiring technical programming skills. Participants are equipped to confidently lead AI initiatives and drive transformative change within their organizations. Through a deep dive into AI methodologies—including machine learning, deep learning, and natural language processing—students develop critical thinking and practical skills to evaluate when and how AI can unlock organizational value.
Beyond foundational concepts, the course offers strategic insights into data sourcing, project planning, and resource estimation, essential for executing successful AI initiatives. Sometimes delivered through a dynamic inverted classroom model, students engage with thought-provoking lectures online, then apply theory to practice in lively, interactive sessions. Activities include solving real-world machine learning challenges, architecting the adoption of large language models (LLMs), and developing comprehensive AI management roadmaps.
The curriculum underscores how AI is revolutionizing industries, reshaping economies, and redefining the workforce, while emphasizing the ethical imperatives necessary for responsible deployment. This paper highlights the course’s most effective elements, illustrating how its innovative structure and targeted learning objectives prepare engineering managers to lead AI-driven innovation with strategic vision.

Authors
  1. Dr. Edwin R Addison North Carolina State University at Raleigh [biography]
Download paper (1.01 MB)