What Your AI-Generated Code Isn't Telling You
How comprehension debt quietly puts your edtech product—and your compliance—at risk.
It started innocently enough. George from accounting heard the dev team was slammed, so he offered to help. Using an LLM, he built a working internal tool over a weekend: a slick little application that pulled data, ran reports, and genuinely impressed everyone in Monday's demo. Leadership took notice. "If George can do this in two days, why are we waiting months for the engineering team to deliver?"
So George got a bigger assignment. And then another. Before long, AI-generated code written by someone with no software engineering background was quietly making its way toward production. The features worked. The interface looked clean. Nobody stopped to ask what was happening underneath, because from the outside, everything looked fine.
There's a line from Jurassic Park that fits here perfectly: "You were so preoccupied with whether or not you could, you didn't stop to think if you should."
The Code Works. But Can You Explain It?
When an experienced software engineer uses an LLM, they're speeding up work they already know how to do. They can look at the output, spot what's missing, and fix it before anything gets near production. The LLM makes them faster at a job they already understand.
When someone without that background generates code with AI, it's a different story. The code might do what was asked, but it routinely leaves out the things a seasoned developer would include without thinking: input validation, error handling, security defaults, proper data protections. The result is code that looks like it works while quietly piling up what Addy Osmani calls comprehension debt: code your team can't fully explain, maintain, or confidently change.
This is different from technical debt. Technical debt is a shortcut you chose to take, with a plan to deal with it later. Comprehension debt is a risk you didn't even know you were accumulating. It compounds silently until something breaks.
When It Breaks, It Breaks Publicly
This isn't theoretical. Over the past year, a string of high-profile failures has shown exactly what happens when AI-generated code ships without the review and polish from experienced software engineers.
One startup founder publicly announced he’d built his entire SaaS platform with “zero handwritten code” using an AI coding tool. Within days of launch, users were bypassing the paywall, hijacking API keys, and flooding the database because basic protections like authentication, rate limiting, and input validation just weren’t there. In another case, an AI coding agent deleted an entire production database during a declared code freeze, wiping data for over 1,200 users. And on one popular genAI app development platform, a security researcher found more than 170 production applications had shipped without row-level security policies, leaving user data openly accessible to anyone.
While this all sounds like story arcs from a sitcom, the stories are happening in the real world and the pattern is the same every time: the code compiled, the features appeared to work, but whole layers of protection were never built because nobody had the product development experience to prompt the AI to include them, and nobody with software quality assurance experience ever reviewed the output. When things start to fall apart, no one knows how to fix it because no one knows the codebase.
In EdTech, the Stakes Are Higher
If you're building products for the K-12 education market, comprehension debt isn't just a technical problem. It's a regulatory compliance problem. In other words, this becomes a potentially serious legal liability.
If your team can't explain what the code is doing, how do you demonstrate that student personally identifiable information (PII) is being collected, stored, and transmitted in compliance with COPPA, FERPA, and the growing list of state student data privacy laws? How do you know it’s not commingling customer data or basing its analysis on the correct set of data specific to a single education institution?
Districts increasingly require demonstrable compliance before they'll adopt your product. A gap here isn't just embarrassing: it's a deal-breaker in procurement reviews and a fast way to lose the trust you've worked hard to build with education customers.
Accessibility is another area where comprehension debt shows up. Meeting WCAG 2.1 at Level AA in K-12 education isn't optional, and AI-generated code regularly falls short on using correct markup, setting aria attributes, and enabling keyboard navigation. If the people maintaining your code can't spot those gaps, they can't fix them either.
From Prototype to Production-Ready
There are genuinely useful ways to use genAI to build applications. When experienced software engineers use genAI, it speeds up development, handles repetitive work, and frees up cognitive processing to develop new functionality. The problem isn't using genAI to write code; it’s treating genAI output as production-ready if there’s nobody on the team with the expertise necessary to verify that it actually is.
We help clients solve this problem. Before taking their genAI prototype solution to market, we work with them to help make the solution secure, compliant, maintainable, and ready to hold up at scale. Having done this for 30 years, we know the established frameworks and architectural patterns. We can anticipate where the problems will crop up (authentication, rostering, data management, and accessibility to name a few) and where regulatory non-compliance will bring progress to a halt. Avoiding comprehension debt isn’t just an engineering problem. It’s also a digital pedagogy and user experience problem. Without expertise in those areas, a genAI developed solution will overengineer a dozen features, the vast majority of which won’t get used or make any difference in teaching and learning. If you’re unsure about your engineering expertise to tackle this, consider contacting us. We work with clients that have internal or external engineering teams, or none at all.
If your product started life as a prototype—whether it was vibe-coded, built by a contractor, or grew out of an MVP that's starting to show its age—we can help you understand what's really going on under the hood and what it'll take to get it ready for the education market. We help clients everyday evaluate and assess their technical debt, genAI prototypes, accessibility, and system integrations, and plot a clear path to compliance, scalability, and stable production. Let us help you avoid the problem of comprehension debt.
Schedule your free 30-minute consultation today.