Generative AI and Training: 5 Pitfalls to Avoid to Optimize Learning

Generative artificial intelligence is profoundly transforming online training. It promises unprecedented personalization and learning path able to adapt to the needs of each learner. But it hides important challenges that, if poorly managed, risk compromising pedagogical quality.

How to fully exploit this potential while avoiding its pitfalls? How can we ensure that AI enriches human expertise rather than trying to replace it? These are the questions that Didask and XOS have recently provided answers to, by combining their expertise.

The 5 major pitfalls of generative AI in training

1. The risk of inaccurate or erroneous content

The “hallucinations” of AI systems represent a major obstacle to their integration into training. These inaccuracies compromise pedagogical reliability and introduce errors that are potentially harmful to learning.

To counter this risk, expert validation mechanisms are becoming essential. XOS (expert operating system) favors a type of creation where each educational element is validated by experts in the field. Didask, for its part, allows the trainer to maintain complete editorial control over the content, thus guaranteeing its accuracy and relevance.
‍

2. Overproduction of irrelevant content and formats

The abundance of content generated by AI creates a paradox: too much information is detrimental to learning. Cognitive science shows that beyond a certain volume, learner attention is fragmented and the assimilation of knowledge becomes difficult.

The quality of the organization of knowledge is as important as its quantity. Didask applies an approach where each educational element is calibrated to optimize cognitive effort, while XOS focuses on short and targeted content that facilitates navigation and avoids mental overload.

3. Lack of real personalization

Customization is often superficial, limited to reorganizing predefined modules. True personalization must adapt to the learning mechanisms specific to each individual: pace, learning style, prior knowledge and specific difficulties.

Didask's adaptive learning continuously analyzes learner interactions to adjust the level of difficulty and propose adapted courses. XOS complements this approach with a modular catalog that adapts to the specific needs of each organization, creating a relevant and contextualized learning environment.

4. The oblivion of human expertise

The more we automate content creation, the more valuable human expertise becomes. Professional experience, sensitivity to context, and critical thinking are dimensions that the algorithm cannot replicate.

The ideal approach places AI as an amplifier of expertise rather than a substitute. It frees experts from repetitive tasks so that they can focus on personalized support and the transmission of knowledge from experience — a dimension that is fundamentally inaccessible to algorithms.
‍

5. Data security and confidentiality

The use of AI in training raises crucial questions about data protection. How to personalize learning while maintaining the confidentiality of personal and organizational information?

This challenge intensifies with each interaction between learners and intelligent systems, as each exchange generates analyzable data. An ethical approach to these issues is essential to develop innovative and respectful training courses.

‍

Faced with the transformation that generative AI is bringing to training, a thoughtful approach is needed. The five challenges identified represent opportunities to develop more effective teaching practices.

These complex issues were the subject of a recent webinar where Didask and XOS shared their expertise and methodologies. Their collaboration shows how AI can improve cognitive efficiency without compromising educational quality — provided it is deployed wisely.
‍