Course Overview
This course provides an engineering-focused examination of artificial intelligence with emphasis on how AI and generative technologies are reshaping the engineering profession itself, rather than on AI as a purely computational or academic topic. The course addresses how AI tools are being applied in engineering analysis, design, modeling, documentation, and decision support, as well as the risks and limitations associated with their use.
 

Upon completion of this course, participants will be able to:

- Explain foundational artificial intelligence concepts and distinguish data-driven AI systems from traditional deterministic engineering software.

- Differentiate generative AI technologies from conventional automation tools and describe their roles within modern engineering workflows.

- Identify and evaluate current applications of AI in engineering design, analysis, documentation, and operational decision support across multiple engineering disciplines.

- Assess how AI tools influence engineering workflows, productivity, and task allocation, including impacts on early-stage design, analysis preparation, and technical documentation.

- Apply verification, validation, and quality control principles to AI-assisted engineering work to ensure compliance with professional standards and engineering best practices.

- Recognize limitations, biases, uncertainty, and failure modes associated with AI systems, including risks related to data quality, model generalization, and automation bias.

- Analyze risks introduced by AI-assisted engineering decisions and identify appropriate mitigation strategies to protect public safety and project reliability.

- Evaluate professional responsibility, ethical obligations, and liability considerations associated with the use of AI and generative technologies in engineering practice.

- Interpret regulatory, legal, and standards-based requirements applicable to AI-assisted engineering work, including licensure, sealing, documentation, and accountability expectations.

- Analyze real-world engineering case studies involving AI-assisted design, predictive maintenance, and automated documentation, identifying both benefits and risks of AI integration.