What is Adaptive Learning and how can you rely on it for your professional training?

Digital transformation is changing the vocational training landscape. In this context, the Learning Adaptive, more commonly known as Adaptive Learning, is emerging as an essential solution for customizing learning paths and optimizing their effectiveness. But how does this method work, and what benefits does it offer businesses and their employees? Answers in this article.

How does Adaptive Learning work?

What are the limits of classical training?

In traditional training, all learners follow the same path, regardless of their levels, needs or objectives. These uniform approaches, often misaligned with professional realities, are perceived as too slow or too complex. The result: demotivation, poor assimilation and difficulty applying knowledge in a real situation.

Adaptive Learning solves these problems by strengthening learner engagement, which improves the assimilation of knowledge, facilitates its application in the field, and maximizes operational impact.

How does Adaptive Learning work?

Adaptive Learning (also called Adaptive Learning) is based on technologies that automatically adjust educational content and activities according to individual needs. Two levels of adaptation are possible:

• Macro Adaptive Learning : personalization of the course or modules from a catalog.
• Micro Adaptive Learning : real-time adjustment of the content in a module, according to the performance of the learner.

The key role of artificial intelligence

Artificial intelligence (AI) plays a fundamental role in Adaptive Learning by making personalization fluid and effective. It analyzes learners' interactions in real time, identifies their specific needs and adjusts content to maximize their progress. In concrete terms, this means:

• Ongoing data analysis : AI collects and interprets responses, time spent, and learning behaviors to model learners' skills.
• Real-time personalization : Based on the results and the shortcomings identified, the AI automatically adjusts the course, proposes adapted activities and targets the key concepts to be worked on.
• Detailed and interactive feedback : AI provides contextualized and accurate feedback on learner performance, highlighting strengths and areas for improvement to promote effective progress.

These capabilities make it possible to transform an often linear experience into learning that is truly aligned with individual needs. The result: strengthened commitment, increased assimilation of knowledge and concrete application in the field, directly at the service of professional goals.

What are the challenges of Adaptive Learning in e-learning?

Despite its promises, Adaptive Learning presents several challenges, in particular related to the technologies used and the quality of the content offered.

Technological approaches: simple algorithms or machine learning?

Two main approaches dominate:

1. Simple algorithms : they work with predefined rules, for example “if the learner succeeds 80% in module 1, he accesses module 2". Although easy to implement and inexpensive, they remain time-consuming for instructional designers, who must manually define criteria that are often arbitrary. These limitations reduce the accuracy and effectiveness of the adaptation.
2. Solutions based on machine learning : they analyze learners' data in real time, model their skills and adjust content with great precision. Effective, they are nevertheless difficult to implement: they require homogeneous and large data, as well as an expensive and complex technical infrastructure.

To find out more, you can consult our article on the different approaches to adaptive learning: Adaptive learning and Generative AI: the winning combination for online training!

Why is content quality essential?

But regardless of the refinement of the approach adopted, if the proposed training has low pedagogical effectiveness because it includes almost only videos sprinkled with 2-3 quizzes, the impact on learners' skills development will be zero.

To ensure results:

• The proportion of content that puts learners into action must be at least 40% to improve learning and provide adaptive learning models with enough data to properly adapt training to their needs
• The contents should ask learners to mobilize their knowledge, ideally in concrete situations, to improve the transfer of learning in the field.

To find out more, you can consult our article How can AI further accelerate the transfer of skills?

Why choose Adaptive Learning Didask?

An intuitive and personalized AI

Didask's AI makes the process similar to what learners are already doing in the field. The learner expresses his needs spontaneously as they would to their manager, without the need to undergo a self-positioning test, and the AI engages in an interactive dialogue to recommend the most relevant training courses. This approach ensures immediate personalization, aligned with learners' individual goals.
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A progressive and continuous positioning test

Unlike traditional approaches, where a long diagnostic test is proposed at the beginning of the apprenticeship, Didask innovates by offering a diagnosis throughout the training. These staggered tests have two advantages:

• they are shorter than initial tests, which preserves the learner's cognitive resources for learning
• its estimate is more reliable because it is positioned just before working on the skill concerned (where the relevance of the adaptation of the course made from its initial level can quickly expire, the learner progressing throughout his training)

This approach improves not only instructional effectiveness but also the overall learner experience.

Activity corrected: feedback and AI coaching

Didask offers fully adaptive teaching formats. Activity corrected by Didask puts the learner in front of concrete situations, directly inspired by his professional environment. The AI coach analyzes its response according to personalized criteria and provides detailed feedback, explaining the points for improvement and proposing clear ways of progress.

This method:

• Mobilizes several skills simultaneously, strengthening cognitive effort.
• Promotes an immediate transfer of knowledge to professional practice.

Combined with the support of the AI coach, corrected activity is a powerful lever for a sustainable and relevant increase in skills.

Conclusion

Adaptive Learning transforms online learning into a truly tailored, engaging, and effective experience. However, its success is based on a balance between technology and pedagogical rigor. With Didask, this balance is achieved through an approach that combines cognitive science and innovation, guaranteeing a real impact on learners' skills.

Modernize your training today thanks to Adaptive Learning and Didask's unique expertise.

Article on the same subject:

• Why and how to apply Adaptive Learning in vocational training
• AI and Adaptive Learning: the winning combination for your online training

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