In 2017, we worked on the integration of artificial intelligence on our training platform. Our first objective was to implement Adaptative Learning, a pedagogical concept where the aim is to adapt the training content to the specific skills and needs of each learner. It was an opportunity for us to learn more and discover the possibilities of artificial intelligence in the training profession.
The first step was to define the data model, the Microsoft experts helped us and we take this opportunity to thank them! Then came the technical integration into our platform. You can discover the first results on the MOOC Office 365. By connecting, you will benefit from the recommendation engine based on the modeling implemented.
The discovery of how it works combined with our experience and our level of expertise on the subject has allowed us to go further by improving the model. The key to learning the AI engine is the ability to transmit data to the AI and time. We can therefore, thanks to the model we have defined and improved, integrate artificial intelligence on our platform.
The new step is now to refine user profiles by business information, or type of business, because in the public or retail sector, learning is not expected in the same way. The size of the company is also important, because an SME does not have the same expectations as a large company for example.
AI also allows, within the framework of training, to go much further:
The evolution of bots with artificial intelligence opens a new field for user training. Today, there is a real possibility to communicate directly with the training platform, as well as with everyday tools such as Skype, Teams, etc… which allow to create more interactions with users. Users can ask questions directly on usage topics and the bots will guide them on the right training and content to meet their expectations. The number of interactions makes it possible to provide increasingly relevant answers and also to build training courses that better meet the expectations of each user while offering an efficient hotline based on the wealth of information contained in our teaching resources.
To date, there is only one step left to arrive at a predictive model of the need for training. Analysing users’ training expectations is made possible by browsing a catalogue and identifying the most important keywords, analysing areas of interest and using the tools we use on a daily basis.
In order to dynamically build future courses and prioritize training, we have set up a partnership with Orkad to study, research new models and algorithms allowing the implementation of training need prediction. As we move forward, we discover new possibilities of the contribution of artificial intelligence in the context of user training. In one year, we have taken our first step to build our starting base and we are now able to accelerate this model thanks to the know-how acquired.
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