5 Key Takeaways from “AI and Innovation in Learning” MOOC

In November, we launched the AI & Innovation in Learning MOOC, joined by almost 500 L&D professionals. We saw a clear need to bring the L&D community together to exchange perspectives. AI is everywhere right now, but the conversation is often fragmented and tool-driven. We wanted to consolidate knowledge on the most discussed topics, capture what’s emerging in practice, and give our community a platform to discuss what AI really means for L&D.

Below, we have combined the five most important lessons that emerged from the discussions and questions in our community. You can learn them with us.

1. AI maturity is lower than it often appears

While AI feels omnipresent, participants’ experiences suggest a more nuanced reality. When asked how much time they actually spend using AI, the average was around four hours per week, often split between work and personal use. An innovation maturity scan revealed that most organizations rely on a group of early adopters who drive experimentation, while broader, systematic adoption remains limited.

In many cases, AI use depends more on individual initiative than organizational strategy. This highlights a key insight: moving beyond isolated use requires more than tools; it requires cultural shifts, shared direction, and space to learn collectively.

2. AI is already shaping everyday L&D work – especially content and learning design

A clear takeaway from the MOOC discussions is that AI is no longer experimental in L&D. For many participants, it’s already part of daily work, mainly to create and improve learning content. They use it to draft lesson plans and outlines, generate exercises and practice scenarios, write scripts, build slides, and even produce quick voice-overs for video content. Others mentioned using AI to translate and edit materials so they can be shared more widely. AI also supports analysis and evaluation: summarizing interviews, meetings, or documents, synthesizing large amounts of input into clearer insights, and helping shape evaluation questions.

Overall, AI is mostly being used to make existing L&D work faster and more manageable.

3. AI works best as a sparring partner, not a replacement

Many participants described using AI to think with, not to think for them: a tool to brainstorm, sharpen ideas, and bring structure to first drafts. They use it to get started faster, overcome writer’s block, refine storylines, and explore alternative approaches to learning design. Some shared how they ask AI to generate a first instructional design or draft rubrics or competencies, which experts then validate and improve.

Others use it as “another voice in the room” for feedback, idea testing, or creative exploration (like scenario writing or simple visuals). The consistent message: AI can speed up thinking, but humans remain responsible for judgment, context, and quality.

4. Using AI well requires critical judgment, not just confidence.

Alongside enthusiasm, participants consistently raised concerns about data quality, bias, reliability, and trust. They were highly aware that AI outputs are not neutral and cannot be taken at face value. Rather than resisting AI, many described actively negotiating their relationship with it, questioning outputs, validating sources, and deciding when not to use it.

This points to a more mature understanding of AI in L&D: effectiveness isn’t about speed or clever prompting alone, but about discernment — knowing when to rely on AI, when to challenge it, and when to step away.

5. There is no one size fits all, context matters

Participants repeatedly emphasized that AI's usefulness depends heavily on organizational context. Role, sector, data sensitivity, culture, and existing systems all shape what is realistic and responsible. What works well in one organization may be impossible or inappropriate in another. This reinforces an important lesson for L&D: meaningful AI adoption doesn't come from copying best practices, but from translating AI capabilities into local needs, constraints, and maturity levels — and adjusting as those contexts evolve.

Impact in numbers

What stood out most wasn't only the activity — it was the quality of the exchange. Participants shared doubts, experiences, and experiments openly, often responding to each other rather than only to the content:

• 500 participants from 14 countries
• 14,000+ interactions and 2,300 comments
• Strong momentum early on: over 200 active contributors
• Post-MOOC survey: 4.2/5 overall rating
• 90%: workload "about right"
• 74%: content well balanced
• 80%+: felt part of a community
• Live webinar with Donald Taylor: 90+ attendees

What others say

This MOOC has clarified my understanding of how to relate to AI when it comes to innovation and learning.

“It inspired me to experiment with strategies and tools and gave me direction on how to move forward.”