Leveraging Generative AI in Immersive Tech Learning
June 23, 2025

Leveraging Generative AI in Immersive Tech Learning

How to integrate GenAI into hands-on tech learning so teams build real skills, not just tool awareness.

Despite the rapid rise of tools like GitHub Copilot and ChatGPT, only about 15% of developers are regularly use GenAI in their workflows. This gap isn’t due to lack of access, it’s due to how learning is structured.

Most training treats GenAI as a side topic or a separate course. But adoption only happens when people use the tools in real, meaningful work. At bILTup, we’ve been integrating GenAI into our hands-on, challenge-based workshops across DevOps, Cloud, and Software Engineering. Here's what we've learned.

Why Immersive Learning Works for GenAI

People don’t internalize GenAI workflows by watching demos. They learn by applying the tools in real context:

  • In a DevOps workshop, learners use Claude to generate infrastructure templates, then refine them with their own requirements.
  • In an AWS lab, participants use GenAI to document architecture or automate CLI commands.
  • In AI capstones, teams test prompts on real company data and iterate to get useful outcomes.

This kind of embedded learning builds fluency and confidence without needing separate “AI training.”

A Simple Roadmap for Integration

If you're designing a workshop or learning experience, here’s how to fold GenAI into the process:

  1. Start with a real task, not a tool. Choose challenges your learners already face such as code refactoring, API design, documentation.
  2. Use GenAI as an option, not a requirement. Let people compare outputs with and without it.
  3. Include time for reflection. Ask: What worked? What felt awkward? Where did GenAI actually help?
  4. Build prompt fluency slowly. Provide examples, but let learners edit and test prompts live.

Final Thought


As with any new tool, the real challenge isn’t access, it’s habit. Embedding GenAI into everyday development workflows means normalizing its use during learning. That means creating space for experimentation, iteration, and even failure. By treating GenAI like a teammate rather than a tutorial, teams are more likely to adopt it in more effective ways.