Artificial Intelligence:
Useful tool in Corporate Learning or a complete loss of control?
What kind of influence does artificial intelligence (AI) have on learning management systems? Where and how is it being used already? And are all employees about to be continuously monitored by machines?
These are just some of the questions that are also on L&D specialists’ minds as they consider the potential applications of AI in learning systems. And that’s more than enough reason to take on some of these questions and look at them in greater detail.
When it comes to the topic of artificial intelligence, there are a lot of misgivings. Losing sensitive data and constant employee monitoring are only two examples, and learning experts are also having to confront these questions as they develop.
However, imc software expert and AI enthusiast Andreas Pohl thinks it’s important to put things into perspective: “A lot of the fears related to the subject of AI are unfounded. Sure, there absolutely are justified concerns regarding ethical issues that need to be clarified, but when it comes to our Learning Management System (LMS), customers don’t have to worry about the possibility of an evil AI suddenly stealing their data or something like that because it’s not a thing that can actually happen.”
To provide a better understanding of what AI really can and cannot do and how it is used in our LMS, we’ll go over the most important concepts and provide concrete examples of their use.
What is machine learning?
The term is used to describe dynamic algorithms that are able to learn and improve by themselves in such a way that systems can recognize recurring patterns, develop solutions, or provide advice, for example. And the more data that is entered, the more accurate the corresponding predictions.
However, it’s absolutely imperative to define, in great detail, what data is relevant and which rules should be used to analyse data and recognize patterns. In other words, it requires for human beings to actively step in when it comes to the analysis of data and the actual decision-making process.
For more information, visit DeepAI.org.
What is machine learning used for?
One typical example of a machine learning application is image recognition processes. If you teach a machine that a triangle always has three corners and a rectangle always has four, the program will recognize this. It’s important to point out, however, that very exact parameters need to be set up and that if an image doesn’t meet those exact parameters, it won’t be recognized sometimes.
Where in our LMS does machine learning take place?
A typical example consists of automatic recommendations similar to those used by Amazon: “You like A, so check out B.” This is the exact same principle used by the recommendation engines in learning management systems.
Basically put, a person chooses the course they want to take, and the system provides additional recommendations based on this. In fact, these recommendations can also be linked to the person’s learner profile, i.e., their position, their development goals, the courses they’ve already completed, etc.
And the more data that the underlying algorithm has, the better its recommendations will be. This is one of the classic applications of machine learning.
What is deep learning?
Deep learning is a subset of machine learning in which a machine is able to improve its abilities independently and without any human assistance. In contrast to machine learning, people do not influence deep learning results at all, and instead only make sure that the required information is available and that the relevant processes are documented.
In fact, the machine itself carries out the actual analysis and uses it to derive forecasts and/or decisions without assistance on the basis of neural networks, which are connected to each other in a manner that resembles the human brain. Ultimately, the machine is able to make decisions based on these connections.
Now, it’s important to point out that this requires an enormous amount of data, and that this type of data is not found in individual learning management systems.
For more information, visit DeepAI.org.
What is deep learning used for?
One typical example of a machine learning application is image recognition processes. If you teach a machine that a triangle always has three corners and a rectangle always has four, the program will recognize this. It’s important to point out, however, that very exact parameters need to be set up and that if an image doesn’t meet those exact parameters, it won’t be recognized sometimes.
Where in our LMS does deep learning take place?
Generally speaking, using deep learning in learning management systems is still extremely difficult given the lack of sufficient data. In fact, in order to be able to analyse specific patterns and processes, learning platform vendors would have to analyse the data from various companies together, but this isn’t possible due to the fact that this data is subject to very strict data protection regulations and policies.
One concrete example of an area where deep learning algorithms could be used would be learning style recognition. With AI, this recognition could be much more efficient than it has been to date, as it would work uncoupled from manual input and tests.
In this scenario, a system would be able to automatically figure out a learner’s preferences and behavioural patterns and determine the correlation for the corresponding learning results. This means that, from a purely theoretical perspective, your LMS would be able to determine what your learning style is and recommend appropriate content for you based on that.
What is the reality?
In order for the system to be able to use the existing knowledge on how to use learning style recognition to provide appropriate recommendations, all learning contents would have to be available in various versions, that is, as text, images, video, games, or audio.
Needless to say, however, creating these individual contents would entail an enormous amount of time and money, so this approach has seldom been used to date in real-life applications. However, it’s reasonable to expect that these topics will be pretty important in the future, particularly in relation to improving learning efficiency and learning in the moment of need.
In other words, a lot of the fears that people have in relation to the topic aren’t really relevant, or at least not today. However, when it comes to the subject of learning and AI, it will admittedly be necessary to answer ethical questions regarding applications in the future.
Or as Andreas Pohl puts it: “I think we should see AI as an active tool for supporting people. Humans must always be in the foreground of everything, and every single system must provide customers with real added value, regardless of whether it uses AI or not. And at the end of the day, we can always turn any system or tool on or off.”
More about AI and how it can also be implemented in the onboarding process, you can find out in another article of LMS Hot Topics.
Onboarding slightly different
Already today, onboarding can be integrated into an existing LMS. But this is hardly ever used.
We took a little trip into the (near) future to see what a successful onboarding process could look like.
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The success of introducing an LMS hinges on those responsible for the launch - and not underestimating them. We have compiled some expert tips and a checklist to help you in convincing your stakeholders.
More information about the LMS
If you would like to find our more about the Learning Management System of imc, please find all information here.
