A Response - Rethinking Engineering Education for the AI Era

In my last post, Reimagining Engineering Homework with Simulators in the Age of AI, I argued that traditional electrical engineering homework focused on calculations is now easily solved by AI, requiring educators to shift to simulator-based assignments that develop higher-order skills like design, troubleshooting, and systems thinking. By using circuit simulation tools, students can engage in active experimentation and real-world problem-solving that requires distinctly human engineering judgment that AI cannot replicate.

I received the following comment on the post: 

I agree that it makes no sense to assess students' ability to make calculations that the simulators they are familiar with already make. The problem, though, is that even the tasks you suggest (e.g., create a design that meets specifications) can be already be accomplished by a variety of generative artificial intelligence platforms. Which begs the questions: what will the electrical engineers we are training actually do when they graduate, and what will they need to know in order to do it?

I’d be a liar if I said I was not asking myself the same questions. Here’s my reply:

You raise a crucial point that goes to the heart of modern engineering education. The rapid advancement of AI tools that can handle both calculations and design tasks challenges us to fundamentally reconsider what students need to learn.

I think the key lies in developing capabilities that remain distinctly human, even as AI handles more routine tasks. Future electrical engineers will likely need to excel in:

Systems thinking and integration - While AI can generate designs meeting specific parameters, engineers must understand how components interact within larger systems, identify trade-offs, and make judgment calls that balance competing constraints beyond what can be easily quantified.

Problem definition and formulation - Perhaps most critically, engineers need to determine what problems to solve in the first place. AI can optimize solutions, but it still requires human insight to identify the right questions and define meaningful specifications that serve real human needs.

Critical evaluation and verification - Engineers must be able to assess AI-generated solutions, spot errors or limitations, and validate that designs work in real-world conditions with all their messy complexities.

Innovation at the intersection - The most valuable engineers will combine domain expertise with an understanding of what AI can and cannot do, using these tools creatively to solve problems that neither humans nor AI could tackle alone.

Rather than competing with AI on tasks it can already do, engineering education must focus on these higher-level skills while using AI tools as aids in the learning process itself.