How to Build AI Learning That Works for Everyone

This Week

This week, I’m doing something a little different.

Instead of tool roundups or quick strategies, I’m sharing a deep dive on a topic that’s been front of mind: how to train teams to use AI in a way that sticks.

Most training isn’t built for how people really learn, and it definitely doesn’t account for the 1 in 5 employees who are neurodivergent. That’s a huge miss.

In this issue, I’m breaking down what it takes to design AI training that people engage with, learn from, and use to shift how they work, without adding friction or complexity.

Let’s get into it.

How to Build AI Learning That Works for Everyone

When I worked at travel SaaS platform Deem, I noticed something that shifted how I think about user experience. People would use our platform in entirely different ways — and not always how we intended. Some would breeze through workflows, others needed extra support, and a few found workarounds we hadn’t even thought of. That’s when our team realized how critical it is to design for accessibility. Not just in how things look, but in how people think, learn, and interact.

Learning works the same way. 

Especially when you’re rolling out something as big as AI. If you assume everyone learns the same way, you’re going to lose people.

That’s why AI training needs to account for neurodivergence — natural variations in how people process information and solve problems. This includes autism, ADHD, dyslexia and dyspraxia. According to Skillscast 15 to 20% of people are neurodivergent. That’s one in five on your team.

"The most interesting people you'll find are ones that don't fit into your average cardboard box. They'll make what they need. They'll make their own boxes." - Dr. Temple Grandin

But most AI training still assumes there’s one “right” way to learn: slideshows, cookie-cutter modules, tech demos that expect everyone to just get it. That setup fails a lot of people. Not just neurodivergent learners, but anyone who processes information differently.

When companies start recognizing neurodiverse strengths, the results speak for themselves. HBR research shows neurodivergent individuals can be more productive, are less likely to fall into cognitive bias traps, and often make decisions based on logic instead of gut feelings. 

But they need the right setup to thrive, like structured environments, clear schedules, direct communication, and reminders that don’t feel patronizing. It’s not rocket science, just thoughtful design. As a colleague of mine likes to say,” don’t plan for me without me.”

And when it comes to learning AI tools and adapting to new workflows, the stakes are even higher. We’re not just asking people to learn what a tool does. We’re asking them to change how they work, and that’s a big ask.

The Right Way to Train Teams for AI (and Everything Else)

Most AI training programs fall short. They’re either too broad to be useful or too complex to put into action. In both cases, people walk away without changing how they work. When I build training, I focus on what gets results. That’s why I rely on evidence-based learning.

This means giving people practical tools and real experiences that help them learn, and then allow them to apply that learning in the flow of their work.

Yale’s research backs this up. They’ve shown that people learn best when three things happen:

1. Their prior knowledge and experiences are recognized and built on.

2. They get time and space to practice in real-world settings.

3. They have the opportunity to reflect and take control of their own learning.

What does that look like in practice? 

Here are the methods I’ve found work best:

Active Learning

Instead of just feeding people information, active learning gets them involved — doing the work, having conversations, solving problems.

Some of my go-tos:

• Small group discussions where people share ideas and experiences.

• Problem-solving tied to real challenges they face at work.

• Collaborative projects that build both skills and team trust.

• Debates — because defending an idea forces deeper thinking.
  

This hands-on learning helps people build solid understanding they can actually use.

Formative Assessment

Training shouldn’t be a mystery. Instead of waiting until the end to see if people “got it,” formative assessment is about checking in as they go.

Tools that work:

• Quick end-of-session reflections (“What’s clear? What’s still fuzzy?”)

• Think-Pair-Share activities to surface and address confusion.

• Questions that push people to explain or rethink their approach.

It’s low pressure but helps people adjust and helps you spot gaps early.

Metacognition

When people start thinking about how they learn, everything shifts. Metacognition gives them more control and helps them become independent problem-solvers.

Ways to support this:

• Asking them to predict outcomes and reflect afterward.

• Encouraging them to spot where they got stuck or what clicked.

• Tracking confidence levels during tasks.

Scaffolded Learning

People don’t master AI tools in one go. They need structure — a way to move from basics to complexity without getting overwhelmed.

Scaffolding helps:

• Break tasks into manageable steps.

• Offer examples before expecting independent work.

• Guide first, then gradually release responsibility.

• Use visual aids and templates for support.

• Leverage peer learning — sometimes help lands better from someone who just figured it out themselves.

These strategies also support neurodivergent learners by offering more clarity, flexibility, and structure. And that’s the kind of learning environment where everyone performs better.

Keep reading on Medium.

Build a Customized Training Program for Your Team

If you’re ready to get an AI training program off the ground but aren’t sure where to start, let’s talk. I run acceleration programs and workshops that help teams learn, adapt, and start using AI in ways that improve how they work — no fluff, no wasted time.

Reach out if you want to build something practical and effective.

Send a note to [email protected].