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[Infographics] AI and skills-based approach : the solution to many learning challenges

Today, only an accurate and universal base which links jobs, skills and training data can solve the main HR challenges. Focus on the combination of skills and AI to meet the challenges of learning and development.



Challenge 1: Provide a functional skills repository


In order to address learning-related issues, HR and L&D managers must first and foremost list all the skills available to employees and those covered by their learning catalog. They therefore need to have access to up-to-date, standardized skills repositories.


AI contribution


AI frees them from the operational burden of manually inputting the information, by allowing them to develop a personalized, dynamic, and prospective ontology of jobs and skills.


Related : Skills repository: a must for setting up your own adaptive learning



Challenge 2: Define the skills contribution of a learning catalog


Mapping learning catalogs and moving to a skills-based approach allows for an objective analysis of their composition. This approach facilitates the identification of the skills covered by the learning catalogs, in order to highlight possible overlaps and cross-references, but also potential gaps within the organization’s training offer.


AI contribution


AI, and more specifically the semantic analysis algorithms used in NLP (Natural Language Processing), allow training content to be automatically translated into standardized skills by comparing it with already standardized jobs and skills databases. It therefore generates a standard reading key for all training content, giving access to a complete and objective overview of the structure’s offer.


Related : AI and HR: What are NLP techniques used for? (part 1)



Challenge 3: Suggest customized training paths for each learner


Being able to offer training courses adapted to the needs and profiles of learners is one of the keys to improving the relevance and effectiveness of learning and development program.


AI contribution


The ontological approach and dedicated matching algorithms make it possible to match users’ profiles with appropriate training content, based on the skills sought by the former and those covered by the latter.


Related : AI and HR: Why use NLP techniques? (part 2)



Challenge 4: Assess current skills


The level of skills of the learners prior to the training courses is essential information on which to base the content that is best adapted to their needs. An effective way to determine this initial level is to use multiple choice questions.


AI contribution


After determining how the skills relate to the trainings, using NLP algorithms, it is then possible to use AI to create banks of questions and answers to measure the level of mastery of the different skills covered by the trainings.



Challenge 5: Adapt the training content to the learner’s level


Adaptive learning makes it possible to personalize training content to adapt it as closely as possible to the needs of learners in order to optimize their skills development so that they can reach their objectives more effectively.


AI contribution


In addition to being able to suggest learners to the training modules most suited to their profile and needs, it is also possible to personalize the content offered to them based on their level of mastery of the subject. For this, the use of an ontology, NLP and neural networks are necessary.


Related: Why and how to rationalize your training catalogue?


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