AI Is Changing EdTech, but Not the Way You Think

Can AI generate a full lesson plan? Yes.

Can it maintain content alignment with evolving standards and formats? Not without help.

Can it make quizzes, videos, even images? Sort of, if you don’t mind doing some cleanup.

Most of the real work still happens after the AI finishes.

In EdTech today, AI tools are incredibly powerful. But power alone doesn’t guarantee impact. To actually create useful and scalable learning tools, developers still need to make smart decisions about how and where to apply AI.

 

Making AI Work in EdTech

Large language models (LLMs) like GPT, Gemini, and Claude are changing how educational content is developed. They can read and analyze dense documents in a matter of seconds. Also, extract the structure and spit out learning objectives, quiz items, or summaries.

It’s impressive. But here’s the catch: raw output isn’t always usable. EdTech teams still have to rework and guide that content to fit specific formats, whether that’s an LMS (Learning Management System) module or a personalized micro-lesson.

 

Customizing for Structure and Format

LLMs work best when the data is clean and consistent. Unfortunately, most source material in education isn’t. Developers often build custom workflows on top of AI models. These workflows help the AI recognize what a subtopic is, what a header is, and what belongs together, even when the formatting is inconsistent.

For example, when working with state-specific K–12 curriculum PDFs, AI might need to extract from one column, while ignoring footnotes or watermarked formatting across hundreds of pages. Without customized logic layered on top, the model might miss the structure entirely.

It’s not just about extraction. When designers program AI logic to repackage content into different formats: slide decks, SCORM (Sharable Content Object Reference Model) files or responsive activities. That’s where AI starts to feel useful, not just fast.

 

Outdated AI Is a Bigger Risk Than It Seems

In EdTech, content that’s even slightly out of date can do more harm than good. Learning materials need to keep up with the latest research and real-world context. Especially in fields like health, science, and policy, where things can shift quickly.

Solution:

Keep the Model on a Content Maintenance Plan.

To stay relevant, AI models need to be retrained with fresh data. Smart teams need to regularly fine-tune AI models using the latest data. For instance, post-pandemic health content should reflect new medical scenarios and treatment protocols. The same applies to STEM standards.

The Result :

Content that doesn’t just look accurate, it is accurate.

 

What AI Can (and Can’t) Do with Visuals

We’re seeing more EdTech companies use AI for visual generation, but get mixed results. AI-generated images are quick and often good enough for placeholder use. But for most real-world projects, human designers still need to step in and refine the final assets.

Video generation is even more limited. While AI can help convert text into infographic-style animations or narrated explainers, it still struggles with rich motion design or character animation. That makes it useful for simple content refreshes, not for high-fidelity experiences.

In both cases, AI saves time. It doesn’t replace the creative process, at least not yet.

 

Coding Smarter, Not Just Faster

One of the strongest use cases for AI in EdTech right now is in development. LLMs can help developers write clean, structured code for learning tools. Everything from quiz builders to simulation templates.

But again, it’s like not set and forget. Developers still have to review that code for accuracy, security, and project-specific needs. The win here is that AI speeds up the basic tasks, freeing us to focus on optimization, testing, and UX.

 

Security and Privacy Still Matter

Security is a serious issue since AI can access private student information and proprietary content. There is a chance that content from one client could appear in the results of another if a model is extensively trained on shared data.

That’s why many EdTech firms now run their private version of a model. These are trained only on their data and hosted in secure environments. That way, content stays contained and private, even if the model is integrated into public-facing tools.

No shortcuts here. Good AI in education needs good fences.

 

New Directions for AI in EdTech

Some of the most exciting developments in AI aren’t happening in content generation; they’re in how we deliver that content. The tools are getting smarter, and so are the ways we use them.

Functional Interactive lessons

AI is beginning to transform how learning materials behave. Instead of uploading a static deck or PDF, educators can now use AI to create modular, interactive lessons that respond to student input.  For example, a simple quiz can evolve into an experience where follow-up questions are selected based on how a student performs in real time. Navigation is no longer linear either. AI can help build branching paths, allowing learners to skip what they’ve mastered and focus on what they need.

For InDesign Files

While LLMs can’t create fully functional InDesign (.INDD)  files on their own yet, they can take on a big chunk of the prep work. AI can now extract structured content: titles, descriptions, images, tags, and organize it into ready-to-flow templates that design teams can drop directly into InDesign workflows.

It’s a small shift with a big impact.

 

AI Isn’t a Shortcut, It’s a Skillset

The more AI enters EdTech, the clearer this becomes: the value isn’t just in what the model can do. It’s what your team knows how to do with it.

Whether you’re building courses or entire learning ecosystems, AI isn’t replacing educators or developers. It extends them when used intentionally.