Does this type of learning apply to my field?

Cognitive psychology and neuroscience have shown that the mechanism of learning is universal: it's essentially the same whether you're learning how to ride a bicycle or to integrate a complex function. In all cases, successful learning requires a change in your long-term memory thanks to spaced reactivations.

Since our first experimentations back in 2019, we've worked with many companies, organizations, and institutions across all sectors. Each one had its own learning culture and thematics, but our intelligent contextualization approach worked like a charm to adapt to specific needs.

So there is every reason to believe that intelligent learning applies perfectly to your field. If you still have doubt, get in touch and we'll be more than happy to examine your particular learning needs!

Do you integrate in my learning ecosystem?

Yes. Our intelligent learning solutions can also be integrated in all common learning platforms.

What is microlearning?

We all know that the goal of learning is to become able to deal with complex challenges full of uncertainties. However, this does not imply that the learning process should focus exclusively on complex and uncertain contents.

On the contrary, learning sciences showed that the most productive approach is to decompose the complexity into simple building blocks and to make sure that they are solidly mastered. This is microlearning.

Once the simple building blocks have been assimilated, more complicated and practical tasks have a much higher success rate. In fact it's fair to say that without the mastery of even just one of the building blocks involved in an elaborated tasks, learners have almost no chance of success.

How can I be sure that the AI-generated puzzles are good?

Our AI models generate high-quality microlearning puzzles from your learning materials. It can however sometimes happen that a puzzle has a problem that limits its usefulness. This is why we make it very easy for trainers and managers to review the puzzles associated with their training programs. They can quickly edit them or fully remove them if necessary.

Learners themselves have the possibility to react to a sub-par puzzle: they can flag it or start a discussion. When a puzzle if flagged by multiple learners, it's a clear signal for reviewers that it may need some fixing.