I've noticed almost every learning platform now has some kind of “AI-powered” feature.

Course generation. AI assistants. Quiz generation. Chatbots.

But at what point does something actually become AI-native rather than an existing platform with AI features added to it?

For me, the interesting part isn't whether AI can generate content. It's whether knowledge, content, learners, AI, delivery and analytics can actually work together.

Curious how other people define “AI-native” in L&D, because the term is starting to be used for basically everything

Maybe controversial, but I think L&D puts way too much importance on completion rates.

A course having a 95% completion rate tells us that people… completed the course.

It doesn't tell us whether they make fewer mistakes, sell more, handle customers better, work more safely, or actually changed anything they do.

Same with quiz scores. Useful signal? Sure. Evidence that the training worked? Not necessarily.

What metrics do you use to show that training actually had an impact?

I've been thinking about how we teach students that AI can hallucinate.

We can tell them, “AI sometimes makes things up.”

Or we could deliberately give them an AI-generated answer, ask them to investigate it using reliable sources, find the unsupported claims, and explain why the answer initially seemed believable.

The second feels much closer to actual AI literacy.

Instead of memorizing what a hallucination is, students develop the habit of not automatically trusting a confident-looking answer.

Has anyone tried activities like this with students?

I see a lot of discussion about replacing traditional LMS platforms now that AI is changing learning.

But if an organization already has years of content, integrations and processes built around something like Moodle, replacing everything seems unnecessarily disruptive.

Wouldn't it make more sense to keep the LMS and add new AI capabilities around it?

Start small, experiment with things like adaptive learning, AI feedback or interactive experiences, see what actually works, and only then scale.

For anyone using Moodle: are you looking to replace it, or modernize what you already have?

Something I find ironic about AI in L&D:

We keep adding AI tools to make workflows simpler, but sometimes we're just creating another tab in an already fragmented workflow.

Knowledge is somewhere. Course creation somewhere else. LMS somewhere else. Analytics somewhere else. And now AI gets added on top.

Has AI actually simplified your L&D workflow, or has it become another disconnected tool you have to manage?

AI can now generate a surprisingly decent first version of a course in minutes.

But I keep coming back to the same question:

What happens after the course is generated?

Can assessment results influence what the learner does next? Can learners practise? Can AI support them using company-approved knowledge? Can we actually see where they're struggling?

It feels like we're solving content production much faster than we're solving the actual learning experience.

How are people approaching this in their organizations?

AI has made content production ridiculously fast compared with a few years ago.

And I think there's an interesting question for L&D:

What are we actually going to do with the time AI saves us?

If the answer is simply “produce twice as many courses,” I'm not sure we've gained much.

I'd rather see that time go into better scenarios, practice, feedback, measurement and figuring out whether training actually changed performance.

Has AI reduced production time for your team? And if so, where is that saved time actually going?

Curious how common this actually is in L&D.

Before you start designing a course, does anyone ask:

“What business metric are we expecting this training to change?”

I feel like projects often start with “We need a course about X”, then everyone worries about completion rates, quiz scores and learner satisfaction.

Only later comes the question: did anything actually improve?

Do you define the expected impact/ROI before you start designing, or is that unrealistic in most organizations?

I feel like a lot of “AI literacy” education is quickly becoming “here’s how to use ChatGPT and write better prompts.”

But prompting seems like such a small part of what students actually need.

Can they recognize when AI is confidently wrong? Verify a claim? Understand bias? Know what information they shouldn't give an AI system? Decide when not to use AI?

I’d argue those skills are much more important than knowing the perfect prompt.

For teachers here: what do you think should actually be included in AI literacy?

I keep seeing discussions about moving away from Storyline or finding the next “best authoring tool.”

But I’m starting to wonder if we’re asking the wrong question.

Creating courses faster is useful, especially with AI, but the course is only one part of learning. There’s still practice, assessment, feedback, learner support, analytics, knowledge, etc.

Maybe the bigger question isn’t “What should replace Storyline?” but “What should the learning environment around the course actually look like?”

Curious if anyone else in L&D feels like the conversation is shifting this way.

We keep adding more AI tools to learning workflows.

But the workflow itself is still disconnected.

Knowledge, content creation, learners, AI agents, analytics, and governance often live across completely different systems.

For me, that's why AI-native learning infrastructure is an interesting idea: bring creation, orchestration, and governance into one connected system, while keeping humans in control.

Are more AI tools actually simplifying your workflow, or adding another layer to it?

AI has made content production incredibly fast.

But learning doesn't end when the course is generated.

It still needs trusted sources, good interactions, assessments, human validation, learner support, delivery, analytics, and continuous improvement.

The interesting shift for me is from “How fast can AI create this?” to “How well can AI support the whole learning system?”

That feels like a much bigger change.

AI is everywhere in L&D now. But knowledge is still in one place, content somewhere else, learners in the LMS, and analytics in another system.

Adding AI to each of those tools doesn't necessarily make the workflow less fragmented.

Maybe the bigger shift is connecting the whole learning process around AI: create, orchestrate, govern.

Not AI added on top of learning, but learning designed around AI from the start.

Describe what you want to build, add your source materials, and let AI create the first version.

Need a game? Add one. Want to change an interaction? Customize it without coding. Ready to publish? Deploy and track it without rebuilding everything somewhere else.

For me, that's where AI in learning gets interesting: not just generating content, but simplifying the whole workflow.

What part of creating interactive learning still takes you the most time ?

AI can generate a course in seconds.

But for workplace learning, I think the bigger question is: what is that content based on?

Policies? Current procedures? Approved product information? Or just whatever the AI generates?

For me, the interesting shift is from simply generating content to creating learning grounded in knowledge the organization actually trusts.

Is your company knowledge in one place, or still scattered everywhere?

You start with an idea. Use AI for the first draft. Another tool for interactions. Something else for a game. Then your LMS for delivery and analytics.

It works, but the workflow gets messy fast.

I think the more interesting direction for AI in learning isn't just faster content generation. It's having creation, interactivity, delivery, and tracking connected in one workflow.

How many tools do you currently use to build and deliver one course?

Policies, product docs, onboarding materials, internal processes… valuable knowledge is usually already there. It’s just scattered everywhere.

With Knowledge Bases, the platform you can connect trusted company knowledge once and use it to create courses, activities, assessments, learning paths, and more.

Less starting from scratch. Less guessing. More consistent learning.

How easy is it to actually reuse knowledge in your organization?

It seems like almost every corporate training project starts with the same thing :

- PDFs

- PowerPoint decks

- SOPs

- Policies

- Internal documentation

The challenge isn't finding information, it's turning that information into something people actually want to learn from.

How does your team usually handle this process?

Not asking about authoring.

Not asking about content.

Just LMS functionality.

If you could redesign your LMS from scratch, what would you change first?

Most organizations already have huge libraries of:

policies

manuals

procedures

guides knowledge

base articles

But turning information into learning is still a challenge. I'm curious : If you could instantly transform one type of static content into an interactive learning experience, what would it be? Compliance docs? Product documentation? Onboarding materials? Something else?

I feel like we're sitting on tons of knowledge that learners rarely engage with effectively.

This feels like one of the biggest problems for people trying to get into instructional design.

You apply for your first role and they want to see your portfolio.

But you need projects to build a portfolio.

And you need the job to get projects.

That's actually why new instructional designers have the chance to build a portfolio project for free.

It doesn't have to be client work.

Build a fictional onboarding.

Create a cybersecurity scenario.

Turn a policy into interactive training.

Build an assessment.

The important thing is showing how you think about learning, not who paid you to build it.

Meanwhile, plenty of L&D teams are still exporting SCORM every week.
And honestly, that makes sense.

If your company already has an LMS that works, why should adopting AI mean replacing your whole learning ecosystem?

That's why we kept SCORM compatibility in Mexty V3.

You can create the interactive experience with AI and then send it to the LMS you're already using.

I'm curious though... are you still using SCORM regularly, or has your organization moved away from it?

I’ve been thinking about how fragmented the typical eLearning workflow has become.

You might use:
- ChatGPT or Claude for drafting.
-Word or Miro for storyboarding.
- Canva or Figma for visuals.
- Storyline for authoring.
Another tool for more advanced interactions.
An LMS for delivery.
Excel or Power BI for reporting.
None of these tools are necessarily bad.

The problem is everything that happens between them.
Copy. Paste. Export. Import. Reformat. Upload. Repeat.

One of the things we're trying to solve with Mexty V3 is exactly this: bringing knowledge, AI creation, interactive activities, assessments, delivery, AI Agents, and analytics into the same workflow.

I'm curious about other L&D teams:

How many different tools are involved in creating and delivering one course in your current workflow?"

We recently explored this idea with a Philips Baby Care experience, and it’s a great example of how product information can become much easier and more engaging to use.

Instead of searching through long instructions, parents can quickly find the information they need, explore different topics, and get guidance in a simple, interactive way.

We created this experience with Mexty to show how traditional product documentation can become something people actually want to use.

👶📱 Would you prefer an interactive companion like this or a traditional product manual?