Texas A&M University · CSCE 289
Learn to harness modern AI—APIs, pipelines, and generative tools—to solve real engineering problems. No prior programming experience required.
About the Course
AI is already reshaping every engineering discipline. This course gives you the practical skills to use it—and the critical eye to use it well.
You'll call real AI APIs, build multi-step workflows, and complete notebook assignments that mirror actual engineering tasks.
Every example connects to engineering: analyzing technical reports, interpreting datasets, exploring design alternatives.
Understand how LLMs actually work—tokens, probabilities, hallucinations—so you can evaluate outputs critically instead of trusting blindly.
Finish the semester with a working, AI-powered solution to an engineering problem you care about—built and presented with your team.
Starter notebooks, guided exercises, and low-code tools mean you spend time solving problems—not fighting syntax errors.
Learn to spot bias, misuse, and over-reliance before they bite you in practice—a skill every engineer needs now.
Learning Outcomes
By the end of the semester you'll walk away with five concrete skills.
Explain the capabilities and limitations of modern AI systems, including generative AI, in the context of engineering applications.
Use AI tools and APIs to solve engineering-relevant problems, including generating, analyzing, and transforming technical content.
Combine AI with basic data analysis to interpret engineering datasets, logs, or experimental results.
Evaluate AI-generated outputs for correctness, reliability, and potential errors—including spotting hallucinations and inconsistencies.
Recognize ethical considerations and risks in AI use, including bias, misuse, and over-reliance on automated systems.
Course Schedule
Each week builds on the last—from understanding how AI works to building your own AI-powered engineering tool.
Grading
A mix of reflection, practice, and a real shipped project.
Hands-on Google Colab notebooks. Modify and extend starter code to build real AI workflows—prompt design, API calls, structured outputs, and more.
Design and implement an AI-powered solution to an engineering problem you choose, with a final presentation and written summary.
Short written reflections (every other week) connecting course concepts to your discipline and evaluating AI capabilities critically.
Active engagement in class activities, in-class exercises, and collaborative problem-solving during hands-on sessions.
Your Instructor
Questions? Don't hesitate to reach out.
Texas A&M University · Department of Computer Science & Engineering