How AI revolutionizes skills assessment

Smarter assessments with AI: stimulating learning

We often highlight the advantages of a strong evaluation culture, the conviction that a company should endeavor not only to disseminate knowledge but also to validate it. The best way to achieve this is to normalize, even to generalize, evaluations. Today, artificial intelligence brings a force renewal to this approach. To provide tangible advantages in the field of training, AI must be closely integrated into Elearning platforms, with the aim of immediate productivity gains. The possibilities are vast, not only in the creation of content, but also, for example, in the notation of learners' submissions. Beyond the time and money economy, AI can improve the quality of the training and open new ways. Let's explore how.

Assessment plays a central role in all training processes. Upstream, it helps to map skills, identify training needs and validate the prerequisites. Downstream, it is used to confirm the learning results, to assign certifications and to measure the progress of knowledge, thus evaluating the added value of each training initiative. That's not all: even during training, evaluation is a powerful tool to strengthen knowledge and improve retention, undoubtedly the most effective learning tool.

Obtain correct assessments: why are the big questions?

To implement high quality, reliable, complete and engaging assessments, large banks of questions are essential. These banks guarantee in -depth coverage of the subject while offering varied types of exercises and approach angles. They allow dynamic quizs, where questions adapt to the previous answers of the learner, one of the foundations of adaptive learning.

Some trainers have developed hundreds' databases or even thousands of questions to tackle key subjects in depth. The creation of this content is a major investment, requiring both expertise in matters and educational design. For many organizations, this represents an obstacle to the broader deployment of assessments.

Creation of questions for assessments: can AI help?

Great languages ​​(LLM) models excel in creating content, including educational material. This does not just mean chatting with Chatgpt; On the contrary, the LMS platform manages and automates interaction with the LLM.

To generate questions, you can simply describe the desired subject, specify the type and number of questions, select the target language, click “Start” and the questions are generated. You can validate them for immediate integration or keep them as a draft for refinement.

Content generation from internal documentation

It is possible to generate questions and quizs according to the general knowledge integrated in large LLM, without specific training. However, the desired expertise is often more specialized. In such cases, the LLM must work from a dedicated corpus of internal documentation. The relevant documents (PDF, Word, PowerPoint, etc.) containing the required expertise are compiled, sometimes hundreds of pages. It is important to remember that the quality of the exit of an LLM is closely linked to the quality of its sources of entry.

Once the corpus is defined, the LLM produces content strictly based on this knowledge. An expert can interact with AI to refine the process, for example, by focusing on certain sub-themes or by adjusting the difficulty of question.

The production of a dozen questions takes only a few minutes, and with iteration, hundreds can be created in one to two hours. But the key is not speed or volume, it produces relevant, varied and well in phase questions, often accompanied by explanatory texts referring to key learning points. Offering plausible distractors (choice of incorrect responses) is often difficult for experts, but it is effortless for an LLM compatible LMS.

AI notes the learners' work

Another area where AI considerably improves evaluation processes is the classification. Open questions are an excellent practice, they demand that learners remember without signal knowledge, organize their thoughts and clearly express them. This makes them particularly precious in assessments.

However, open responses require a classification and personalized comments, which takes time and often limits their use, a missed opportunity. Now, an evaluation platform can manage this process effectively, qualitatively and with complete personalization thanks to AI. You can provide a model response and precisely define expectations. For example: “The learner must identify at least three risk of fraud in his response.” Notation instructions can also include rating rules and the tone of feedback (neutral, encouraging, strict, etc.).

Conclusion

Some say that the expected productivity gains from AI have not yet materialized. But in the field of training and education, the advantages are at hand, as we have just seen. Beyond productivity, the ease of creation of quality content simply allows new approaches, for the benefit of learners.

Non-liability clause: The opinions expressed in this article reflect the author's personal opinions and do not necessarily represent the position of the Elearning industry.

Publisher's note: Consult the Elearning industry The best content providers with AI tool expertise.