AI in eLearning: A Practical Guide for Trainers

Generative artificial intelligence is transforming the creation of learning modules, allowing an unprecedented deployment of personalized digital training. But beyond this technological acceleration, the challenge remains fundamentally educational: how can we guarantee truly transformative learning?

The answer lies in the alliance between AI and cognitive science. By automating the application of proven teaching principles, artificial intelligence finally makes it possible to quickly create adaptive courses that truly transform professional practices.

The fundamentals of effective training: from theory to practice

Mastering a professional skill - let's take sales as an example - requires profound transformation of reflexes. This approach, far from being linear, revolves around key moments where practice meets theory, where error becomes a source of learning and where immediate feedback contributes to progress.

Traditionally, orchestrating this transformation required a considerable investment of time and educational expertise. AI is now flipping this equation by automating the application of fundamental cognitive principles.

Thus, for salespeople in training, each learning situation becomes a moment of potential transformation, where competence is built through successive iterations, guided by artificial intelligence at the service of pedagogy.
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Pitfalls to avoid: beyond superficial automation

While artificial intelligence promises to accelerate the creation of training courses, it's imperative to avoid the pitfalls of superficial automation. While AI offers new possibilities, its relevant use requires a detailed understanding of learning mechanisms.

The top-down approach trap

The major pitfall lies in the simple transmission of information, where the learner remains a passive viewer of the content. This approach, which is common in digital training, assumes that it is enough to expose the learner to knowledge in order for them to integrate it. Take our salesperson: presenting them with a succession of slides on consultative selling techniques, even optimized by AI, will not transform their practice. Progress requires active confrontation with real situations, where each theoretical concept is anchored in concrete experience.

The challenge of educational calibration

Good learning depends on accurate calibration of the exercises. A relevant exercise creates what cognitive science calls the ”desirable difficulty” - that balance point where learning becomes both stimulating and accessible.

For our trainee salesperson, each scenario must represent a precisely calibrated cognitive challenge, not too simple - which would lead to superficial learning - nor too complex - which could discourage them and cause them to abandon the training.

Feedback as a learning accelerator

Traditional MCQs (multiple choice questions) perfectly illustrate the limits of a superficial evaluation. When a salesperson answers correctly by proceeding by elimination, has he really integrated the competence? This approach often masks a partial or even erroneous understanding. The challenge lies in creating exercises that place the learner in authentic professional situations, where each response triggers instantaneous and personalized feedback. This guides them towards a thorough mastery of competence.

Didask: the orchestration of a cognitive transformation

Faced with the challenges of training, Didask offers an approach where AI becomes the orchestrator of scientifically validated pedagogy. This orchestration is based on a precise cognitive architecture, where each element contributes to a measurable transformation of practices.

The architecture of transformation

Our salesperson should not simply memorize the steps of a consultative sales interview - they must know how to mobilize them effectively in front of a customer. This transformation takes place through targeted scenarios, where each skill is expressed in real professional contexts. Personalized feedback then acts as a guide, allowing you to instantly adjust your practice.

AI acts as a facilitator here, accelerating the deployment of proven educational practices:

• Rapid analysis of critical professional situations,
• Generation of exercises targeted at transformation points,
• Instant production of contextualized feedback (under development at Didask).

Automation at the service of optimization

‍Didask's innovation lies in its ability to rapidly deploy a methodology derived from cognitive science. AI analyzes your content to identify key cognitive issues and select the most effective teaching methods. This intelligent automation makes it possible to quickly create training courses that respect the fundamental principles of learning: deconstruction of erroneous representations, active practice, personalized feedback.
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‍Measuring impact: transforming into tangible data

Measuring the impact of eLearning training does not have to be complex - it must translate the evolution of skills into tangible data.

A pragmatic approach to evaluation

Let's continue with our salesperson example. The impact is measured with a question: is their mastery of the targeted skills better today than yesterday? This apparently simple query is divided into precise and actionable indicators:

• The quality of their sales discovery calls,
• The relevance of their customer recommendations,
• The evolution of their conversion rates.

Continuous optimization through data

The strength of this approach lies in its ability to instantly transform progress data into actionable levers for optimization. Where traditionally evaluation was often the final step, here it becomes a dynamic instrument for continuous improvement.
This data-driven methodology not only makes it possible to validate the effectiveness of the training, but also to constantly refine it, creating a virtuous circle of educational optimization where each learner benefits from collective lessons.

AI at the service of eLearning

Artificial intelligence does not reinvent pedagogy - it democratizes it. Finally, it allows us to deploy, on a large scale and with unprecedented efficiency, pedagogical principles whose relevance has been attested for decades by cognitive sciences.This democratization of pedagogy is profoundly transforming our relationship with training. Where the implementation of a truly transformative pedagogy required a considerable investment of time and expertise, AI is now opening up a new path: that of intelligent automation at the service of learning.

The future of training is thus taking shape not in rupture, but in the controlled acceleration of scientifically validated pedagogy. A training where technology amplifies humans, where automation unleashes creativity, where artificial intelligence becomes the catalyst for a profound and lasting transformation of skills.

However, a Expertise is required for a good use of AI. For example, Didask's instructional AI, developed by cognitive science researchers, realizes this vision of enhanced training, where speed is at the service of educational excellence.
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