Smarter Assessments With AI: Empowering Learning
We often highlight the benefits of a strong assessment culture, the belief that a company should strive not only to disseminate knowledge but also to validate it. The best way to achieve this is by normalizing, even generalizing, assessments. Today, Artificial Intelligence brings renewed strength to this approach. To deliver tangible benefits in the field of training, AI must be tightly integrated into eLearning platforms, with the goal of immediate productivity gains. The possibilities are vast, not only in content creation but also, for example, in grading learner submissions. Beyond saving time and money, AI can improve the quality of training and open up new avenues. Let’s explore how.
Assessment plays a central role in all training processes. Upstream, it helps map competencies, identify training needs, and validate prerequisites. Downstream, it serves to confirm learning outcomes, award certifications, and measure knowledge progress, thus gauging the added value of each training initiative. That’s not all: even during training, assessment is a powerful tool for reinforcing knowledge and enhancing retention, arguably the most effective learning tool.
Getting Assessments Right: Why Large Question Banks?
To implement high-quality, reliable, comprehensive, and engaging assessments, large question banks are essential. These banks ensure thorough coverage of the topic while offering varied exercise types and angles of approach. They enable dynamic quizzes, where questions adapt to the learner’s previous responses, one of the foundations of adaptive learning.
Some trainers have developed databases of hundreds or even thousands of questions to thoroughly address key topics. Creating this content is a major investment, requiring both subject matter and Instructional Design expertise. For many organizations, this represents a barrier to the broader deployment of assessments.
Creating Questions For Assessments: Can AI Help?
Large Language Models (LLMs) excel at content creation, including educational materials. This doesn’t mean simply chatting with ChatGPT; rather, the LMS platform manages and automates interaction with the LLM.
To generate questions, you can simply describe the desired topic, 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 drafts for refinement.
Generating Content From Internal Documentation
It is possible to generate questions and quizzes based on the general knowledge embedded in large LLMs, with no specific training. However, the desired expertise is often more specialized. In such cases, the LLM needs to work from a dedicated corpus of internal documentation. Relevant documents (PDFs, Word, PowerPoint, etc.) containing the required expertise are compiled, sometimes hundreds of pages long. It’s important to remember that the quality of an LLM’s output is closely tied to the quality of its input sources.
Once the corpus is defined, the LLM produces content strictly based on that knowledge. An expert can interact with the AI to refine the process, for example, by focusing on certain subtopics or adjusting question difficulty.
Producing a dozen questions takes just a few minutes, and with iteration, hundreds can be created in one to two hours. But the key is not speed or volume, it’s producing relevant, varied, and well-phrased questions, often accompanied by explanatory text referencing key learning points. Coming up with plausible distractors (incorrect answer choices) is often difficult for experts, but it’s effortless for an LLM-enabled LMS.
AI Grades Learners’ Work
Another area where AI is dramatically enhancing assessment processes is in grading. Open-ended questions are an excellent practice, they require learners to recall knowledge without cues, organize their thoughts, and express them clearly. This makes them uniquely valuable in assessments.
However, open-ended responses require grading and personalized feedback, which is time-consuming and often limits their use, a missed opportunity. Now, an assessment platform can handle this process efficiently, qualitatively, and with full customization thanks to AI. You can provide a model answer and precisely define expectations. For instance: “The learner must identify at least three fraud risks in their response.” Grading instructions can also include scoring rules and the tone of the feedback (neutral, encouraging, strict, etc.).
Conclusion
Some say that AI’s expected productivity gains have yet to materialize. But in the field of training and education, the benefits are within reach, as we’ve just seen. Beyond productivity, the ease of creating quality content simply enables new approaches, for the benefit of learners.
Disclaimer: The opinions expressed in this article reflect the author’s personal views and do not necessarily represent eLearning Industry’s position.
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