Evolving Engineering Education: AI's Impact on the Classroom

About six weeks into the current spring semester, I stepped in to teach an electromagnetics course when a professor from the Electrical and Computer Engineering Department at the University of Hartford needed to take emergency leave. Returning to teach this subject after six years has been eye-opening. The contrast between teaching methodologies in 2019 versus now reveals a significant transformation in engineering education—one largely driven by the integration of artificial intelligence tools into the classroom experience. 

My Teaching Journey

After serving three amazing and fulfilling semesters as a Visiting Professor at the University of Hartford, in September 2019 I moved to the Engineering Department at Holyoke Community College. There, I spent five more amazing and fulfilling years (2019-2024) teaching Circuits 1 and 2 courses to electrical engineering students who typically transfer to university programs like the excellent one at Hartford to complete their Bachelor of Science in Electrical Engineering (BSEE). These foundational classes at Holyoke, usually taken by second-year students, provide the essential groundwork compared to the more advanced electromagnetics course I've now returned to at Hartford.

What's fascinating is how the AI classroom revolution unfolded around me at Holyoke without my complete recognition. While teaching circuits courses day-to-day, AI tools were gradually integrating into my teaching—so incrementally that the transformation wasn't immediately obvious. It was only upon returning to teach electromagnetics at Hartford after six years that the dramatic contrast became apparent.

 

Problem-Solving Transformation

The traditional approach to electromagnetics problems—careful application of Maxwell's equations, vector calculus, and boundary conditions through meticulous manual calculations using advanced calculus—now exists alongside powerful AI alternatives that can generate solutions almost instantaneously.

In a recent electromagnetics classroom lecture, I worked through a standard homework problem using the conventional pencil-and-paper method, spending about 10 minutes to complete the derivation and solution. When I ran the same problem through Gemini AI, the contrast was striking. Within seconds, Gemini produced the correct solution, presented step-by-step with conceptual connections that enhanced understanding. However, I found that running the same problem multiple times through Gemini sometimes did not produce the correct answer, though the level of detail in the solution made it easy to identify the error. Gemini is just one of many and as these AI systems continue to improve, these errors will become less and less frequent.

 

Redefining Educational Focus

This technological shift is reframing the fundamental questions we ask in engineering education:

• Instead of "How do we solve this problem?" we're increasingly asking "How do we interpret and verify these solutions?"
• Rather than spending most of our time on calculation mechanics, we can focus on "What deeper insights can we gain from these results?"
• The emphasis moves from computation to critical evaluation: "How do we assess the validity and limitations of AI-generated solutions?"

Finding Balance in Engineering Education

Despite these changes, foundational knowledge remains essential. Students still need to understand Maxwell's equations, boundary conditions, and vector analysis. The difference is that AI now serves as a powerful tool for exploration, verification, and extending understanding beyond textbook problems.

For today's engineering students, proficiency with AI tools is becoming as important as understanding the core principles of their discipline. They need to learn when to rely on their foundational knowledge, when to leverage AI assistance, and most importantly, how to critically evaluate AI-generated solutions.

 

Looking Forward

This unexpected return to teaching electromagnetics at Hartford after a six year gap has provided a unique vantage point to witness the evolution of engineering education. The combination of traditional engineering fundamentals with cutting-edge AI tools promises to produce graduates better equipped to tackle the complex technological challenges of tomorrow.

As educators, our role continues to evolve. We're no longer just teachers of technical content, but guides helping students navigate the increasingly AI-augmented landscape of engineering practice. This includes fostering the critical thinking skills needed to effectively collaborate with AI systems while maintaining the fundamental understanding that makes such collaboration meaningful.