Building Strong Foundations in the Age of AI

By Dr. Cecia Chan

AI is changing quickly, and it is natural to feel a little overwhelmed sometimes. There is always another tool to learn, another skill to catch up on, and another question about what is allowed. Both students and teachers are still finding their way.

Some students may wonder whether their peers have access to better tools. Others worry that relying on AI too often may slowly affect their own thinking, writing, or creativity. These are reasonable concerns.

Yet there is also an encouraging side to this story. We do not have to chase every new tool. What will serve us best, both now and in the years ahead, is something more durable: a strong grasp of the fundamentals and the confidence to use AI thoughtfully.

As a teacher of foundational programming courses in Python and C++, I see students develop confidence and discover insights through their own attempts and mistakes. Programming is not only about producing code that runs. It is about breaking a problem into smaller parts, reasoning through the logic, testing ideas, and debugging when something does not behave as expected. These habits take time to develop, but they remain valuable long after a particular language, library, or AI tool has changed.

Rather than seeing AI as something to fear or rely on completely, we can approach it as a resource that calls for thoughtful use. It can help students explore unfamiliar ideas, find more explanations and varied practice, and extend their learning beyond the classroom. But it cannot replace an engineer’s judgment. Nor can it decide whether a solution is correct, efficient, safe, fair, or appropriate for the people who will use it. That responsibility remains with us.

In programming, AI can produce code quickly, but it cannot judge whether the code is suitable, reliable, or maintainable. Understanding why a solution works and recognizing when it might fail is what turns a generated answer into genuine engineering.

That understanding is built gradually. It grows when students trace the flow of a program, predict what code will do before running it, test boundary cases, and learn from errors that initially seem frustrating. These experiences build the understanding students need not only to write code, but also to judge code produced by someone else or by an AI system.

For this reason, engineering education needs occasions when students can attempt problems independently, make mistakes, debug their own code, and experience the satisfaction of solving something on their own. These moments are not a rejection of AI. They are part of preparing students to use it responsibly.

AI is already part of the professional world our students will enter. Ignoring it would not prepare them for that reality. The challenge is to help students develop a mature relationship with AI, guided by curiosity, critical evaluation, and responsibility.

One possible approach is to use AI as a learning companion outside assessed work. After attempting a programming exercise, a student might ask for a similar practice problem and solve it independently. When learning recursion, a student might ask for an explanation using a different example and then trace the calls by hand. AI can help students learn more, practice more, and understand more, but students still need to do the thinking that allows real understanding to take root.

This approach also addresses concerns about unequal access to premium tools. Core learning should not depend on the tools students can afford. Students’ progress should rest on their understanding, persistence, and reasoning.

AI invites us to reconsider what we value in engineering education. When answers can be generated quickly, producing an answer is no longer enough. Engineers must be able to judge whether it is appropriate, verify it against evidence, recognize hidden assumptions, and take responsibility for its consequences. In programming, this means reading error messages carefully, testing boundary cases, tracing logic step by step, and asking why a solution works.

Students have adapted to major changes before. As learning expanded from textbooks to video, many learned to process information through images, speech, and diagrams, while deciding when to pause, rewind, and revisit difficult ideas. I am confident that this generation can adapt to AI with the same resilience if we give them clear guidance and meaningful opportunities to think independently.

Engineering education in the AI era should not ask students to compete with machines. It should help them become engineers who can use AI thoughtfully, question its outputs when evidence is unclear, and take responsibility for the systems they design.

AI will continue to evolve, but the ability to reason rigorously, debug patiently, verify claims, and act responsibly will remain at the heart of engineering. By protecting the space for students to build these capacities, we can help them engage with AI confidently, without feeling that they must simply keep up with it.