For the past four years, IntelliTect’s staff has partnered with Eastern Washington University to teach a variety of computer science courses. I teach the .NET Web Development class, and every year I’ve wrestled with the same question: How do I meaningfully integrate AI into a curriculum that’s supposed to prepare students for the real world?
This year, I decided to stop wrestling and lean all the way in.
Letting AI Into the Classroom—Fully
Instead of treating AI as a shortcut or a threat, I reframed it as a force multiplier. I shifted the course toward software engineering principles—architecture, clarity of thought, problem decomposition, communication—and encouraged students to use AI as a collaborator.
Then I did something bold:
I dramatically increased the size of the first assignment.
Students weren’t just building a small feature or a toy app. They were building:
- A full Wordle-style game with the NYT version as a model
- Complete with high scores, statistics, and hints
- Deployed from GitHub to Azure with CI/CD pipelines
- Polished enough to feel like a real product
In previous years, this would have been a multi-week, multi-assignment journey. This year, it was Assignment #1.
And the results stunned me.
Some students added animations. Others built clever UX touches. Many produced cleaner, more thoughtful products than I’ve seen in years past. When I asked how long this would have taken them without AI, the lowest estimate I heard was twenty times longer.
Is the quality perfect? No way. But it never is at this point.
Check out some of the games here:
https://bestwebappever-ezcyeeemehemhrbc.westus3-01.azurewebsites.net/
https://wonderful-flower-086b7900f.4.azurestaticapps.net/
https://orange-pebble-094083d0f.2.azurestaticapps.net
https://thankful-sea-02c445710.1.azurestaticapps.net
https://zealous-desert-0241aa910.4.azurestaticapps.net/
Please be kind—they built this after four classes of instruction.
Are They Still Learning to Engineer?
Absolutely.
In fact, I’d argue they’re learning more of what matters.
AI didn’t eliminate the hard parts—it exposed them. Students still had to:
- Understand requirements
- Test and debug
- Reason about components and architecture
- Collaborate with peers
- Guide the AI toward solutions
Syntax wasn’t the bottleneck. Thinking was.
The Next Challenge: Build Something AI Doesn’t Already Know
The next assignment pushes this even further. This assignment was actually the final for the other classes I have taught. Except now my expectations are higher. Students must build a completely new application with a front end, back-end API, and database—all deployed to Azure with CI/CD pipelines. The only rule is that it has to be a new concept that doesn’t exist in the collective memory of the internet.
No Wordle clones. No “to-do” lists.
They must invent something unique enough that AI can’t simply autocomplete the solution.
What Do Future Software Engineers Really Need to Know?
This is the question that keeps me up at night and energizes me.
Do students need to memorize syntax?
Do they need to trace missing semicolons?
Do they need to know the exact shape of a SQL SELECT statement?
Maybe. But not in the way we once assumed.
I don’t know assembly. Most engineers don’t.
And yet we build complex systems every day.
Languages evolve. Abstractions rise. Tools change.
But the core of engineering has always been the same:
AI accelerates the easy parts.
It spotlights the human parts.
AI Frees Us to Teach What Actually Matters
By embracing AI, I’ve been able to focus the class on the deeper layers of engineering, the ones that will outlast any framework or language trend:
- Requirements and core logic
- Data design
- User interface and experience
- Application architecture
Students aren’t just asking, “Does it work?” They’re asking, “Did I understand the need?” and “Is this actually good?” Those are the questions that shape great engineers.
A Future That Feels Less Uncertain
There’s a lot of anxiety in the industry right now. Students feel it. Professionals feel it. The pace of change is dizzying.
But watching my students this quarter has made me more optimistic than ever.
They’re not just learning to code.
They’re learning to think.
They’re learning to build.
They’re learning to engineer.
They’re learning to partner with AI.
And those skills will carry them into a future where the tools may change, but the need for thoughtful, creative, human engineers never will.
Designing AI systems you can rely on
If you are exploring how to use large language models in real software, IntelliTect helps you turn clear intent into reliable, production-ready systems.