Many learning tasks, projects or courses in initial and continuing education build on knowledge that may date back weeks or even months. This is precisely where a problem often arises: learners no longer have the necessary prior knowledge at that point to be able to complete the learning task effectively. 😕
With the ‘Knowledge Activation’ feature in MLS, you can prevent exactly that.
Right at the start of a learning task, an AI-powered chat specifically activates learners’ existing prior knowledge – individually, through dialogue and without the pressure of an exam.
How does knowledge activation work from a technical point of view? ⚙️💬
AI knowledge activation is integrated directly into MLS learning tasks (for all customers with the ENTERPRISE model). As a learning facilitator, you use learning objectives to determine what prior knowledge is important for this task. This could include, for example:
Technical terms
Mathematical fundamentals
Technical concepts
Safety rules
Content from previous learning tasks/projects
Step 1
Add the ‘Knowledge Activation’ task step to your learning task.
Step 2
Here, you can specify the learning objectives your learners should achieve during the knowledge activation phase.
You can either enter the learning objectives manually or generate them from an existing learning activity.
In this example, a learning objective has been entered manually.
Alternatively, you can select a learning task via ‘Generate with AI’ to receive suggestions for your learning objectives.
Here, you can specify the number of learning objectives and select the learning task (or, where applicable, the specific step within that task) from which they are to be generated.
Step 3
Check whether the learning objectives generated are appropriate in your view. You can still edit them, delete any that are unsuitable, or add new learning objectives using the ‘+’ icon.
If the learning objectives suit you, simply click Save.
And that’s it! 🏎️ If necessary, add further steps to the task as usual.
Knowledge activation from the learner’s perspective
As soon as learners open the task, they can start the AI-powered chat. It is also possible to skip this step.
The AI guides trainees step by step through a dialogue-based thought process. Deliberately, there is no traditional ‘right or wrong’ style knowledge test.
Instead, the feature operates on the principle of Socratic dialogue 🧩:
The AI asks targeted questions 💬
Learners explain the content in their own words 🗨️
Connections are actively deduced 🔌
Uncertainties are identified and addressed 📊
This fosters genuine understanding – rather than mere rote learning.
In MLS, this is what it looks like in practice. Learners start with a brief check-in.
They enter their answers via the chat. The AI provides immediate feedback, particularly if the answer is still insufficient or too superficial.
At the end, learners receive detailed feedback on their strengths as well as the areas where they still face challenges.
✅ What they have already mastered
⚠️ Where there are still gaps in their knowledge
💡 Which topics they should revisit
The feedback is designed to provide learners with specific guidance on how to improve further.
What makes it special: No assessment❌📋
The knowledge activation feature is designed solely for learning purposes. Answers from the AI chat do not count towards the assessment of the learning task. This creates a safe learning environment where your trainees can think freely and feel comfortable showing their uncertainties.
Of course, as a tutor, you can see what your learners have answered by viewing the assessment for the task. This allows you to provide personalised support to your learners.
An example from everyday training 🔧🦺
Before a task on occupational safety, the AI might ask, for example:
Why is personal protective equipment important?
What risks arise without safety goggles?
In which situations is which PPE required?
Learners actively reflect on the connections and formulate their own explanations. The AI provides flexible support and responds individually to the trainees’ answers.
The key benefits for learning mentors 🚀
More sustainable learning
AI helps trainees to truly understand knowledge and retain it in the long term.
Less ‘clicking through’
Learners engage actively with content, rather than merely skimming through tasks.
Support against “skill skipping”
AI has long since found its way into the lives of teenagers and young adults. For example, three-quarters of school pupils already use AI to do their homework (source: Der SPIEGEL, 17/2026 “Does AI make us stupid?”). The downside: instead of understanding, they simply adopt the AI’s solution. Little learning actually takes place. MLS’s AI-powered knowledge activation therefore does not provide ready-made solutions, but rather supports thinking processes. This ensures that the learning process remains with the trainees themselves. 🧠
Individual support
The AI responds flexibly to different levels of learning and adapts the dialogue to the learners’ answers.
Time savings for learning facilitators
You gain insights into typical uncertainties and knowledge gaps without having to evaluate every piece of feedback individually yourself.
Ideal for exam preparation & revision
This feature is particularly helpful:
before complex learning tasks
for training content covered some time ago
for exam preparation
for self-directed learning
Making good use of AI in training 🤝
Knowledge activation demonstrates how AI can be used effectively in education: not as a shortcut, but as a tool to actively support learning.
Particularly in an age of AI tools and automated responses, it is becoming increasingly important to consciously design learning processes. MLS helps to ensure that learning once again means active thinking, understanding and reflection. 💡