Smarter evaluation with AI: The power of learning
We often emphasize the benefits of a strong assessment culture: the belief that companies should strive to not only spread knowledge but also validate it. The best way to achieve this is to normalize and even generalize the evaluation. Today, artificial intelligence brings new strength to this approach. To deliver concrete benefits in the training field, AI needs to be firmly integrated into the e-learning platform with the goal of immediate productivity. The possibilities are enormous for creating content, as well as scoring learners’ submissions, for example. Not only does it save you time and money, but AI can improve the quality of your training and open new paths. Let’s explore how to do that.
Evaluation plays a central role in all training processes. Upstream, mapping, mapping, helps identify training needs and validate prerequisites. Downstream, it helps to review learning outcomes, provide certification, measure knowledge progress and assess the value added of each training initiative. That’s not all. Even during training, assessment is a powerful tool for strengthening knowledge and enhancing retention, and perhaps the most effective learning tool.
Get the rating correctly: Why big questions bank?
Large question banks are essential to implementing high quality, reliable, comprehensive and attractive assessments. These banks provide thorough compensation for topics while providing a variety of exercise types and angles of approaches. They allow for dynamic quizzes where questions are adapted to previous answers of learners, which are one of the fundamentals of adaptive learning.
Some trainers have developed a database of hundreds or even thousands of questions to thoroughly address important topics. Creating this content is a major investment and requires both subject matter and educational design expertise. For many organizations, this represents a barrier to wider deployment of assessments.
Create a question for evaluation: Is AI helpful?
Large-scale language models (LLMs) are excellent at creating content that includes educational materials. This does not mean simply chatting with ChatGpt. Rather, the LMS platform manages and automates interactions with LLM.
To generate a question, describe the topic you want, specify the type and number of questions, select the target language, and[開始]Click to generate a question. They can be validated for immediate integration or kept as a draft for refinement.
Generate content from internal documents
You can generate questions and quizzes based on general knowledge embedded in large LLMs without specific training. However, the desired expertise is often more specialized. In such cases, LLM must work from a dedicated corpus of internal documents. It includes related documents (such as PDF, Word, PowerPoint) that contain the required expertise, sometimes summarizing hundreds of pages long. It is important to remember that the quality of the output of the LLM is closely tied to the quality of the input source.
Once a corpus is defined, LLM generates content strictly based on that knowledge. Experts can interact with AI to improve the process by focusing on a specific subtopic or adjusting the difficulty of the question.
It only takes a few minutes to create dozens of questions. Iterations allow you to create hundreds in 1-2 hours. However, it is not the speed or volume that matters, but it generates related, diverse, and frequently-extensive questions, often accompanied by explanatory text that references important learning points. While coming up with a plausible distractor (selecting the wrong answer) is often difficult for professionals, it can be easy for LLM-enabled LMS.
AI grade learners’ jobs
Another area where AI is dramatically enhancing its evaluation process is in grading. Open-ended questions are a great habit, and learners need to remember uncensored knowledge, organize their thoughts, and articulate them. This gives it a unique value in the evaluation.
However, open-ended responses require grading and personalized feedback. This takes time, often limits use and misses opportunities. Now, evaluation platforms can handle this process efficiently, qualitatively, and fully customised thanks to AI. It provides model answers and allows you to define your expectations accurately. For example, “Learners should identify at least three fraud risks in their responses.” Grading instructions also include scoring rules and feedback tones (neutral, encouragement, strict, etc.).
Conclusion
Some say that AI’s expected productivity has not yet been achieved. However, in the field of training and education, as we have seen, the benefits are out of reach. Beyond productivity, it makes it easier to create high-quality content, allowing new approaches to the benefit of learners.
Disclaimer: The opinions expressed in this article reflect the author’s personal views and do not necessarily represent the e-learning industry position.
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