AI & Learning - How to make better L&D decisions
Let’s face it, AI (Artificial Intelligence) is no longer something of the distant future, or even the near future, it’s in the present. AI is the development of computer systems that are able to perform tasks that would normally require human intelligence.
In my presentation at our eLearning Community relaunch I discussed how AI in L&D exposes endless possibilities, in incredibly positive ways. Touching on factors such as automated content scheduling and personalised learning to increase return on investment, I wanted to share some of the insights with you now:
To be effective in L&D, an AI-driven learning platform is built upon learning-specific algorithms. These are powered by a fine-tuned combination of machine learning, deep learning and natural language processing.
Machine Learning – A system where a computer learns without being explicitly programmed. For example, in machine learning capacities, a computer program is trained to recognise patterns or complete an action, such as identifying someone’s face or responding to a request for information.
Deep Learning – In deep learning, interconnected layers of software-based calculators – “neurons” – form a neural network, which consumes vast amounts of data and processes it through multiple layers, just like the human brain. These layers progressively extract complex features from the raw data at each step. For example, in image processing, the first layer could learn how to detect edges of an object and the last layer would learn how to identify more complex shapes, specifically catered to the nature of the object we are trying to recognise.
Natural Language Processing – This is the ability of computers to understand and interpret human language in the way it is written or spoken. It’s objective is to close the gap between how humans communicate (natural language) and what the computer understands (machine language).
These specialised algorithms have been developed to enable your AI-powered learning platform, such as Docebo, to automatically perform some of the actions that you would do manually, either as a learner or as an admin. The best part is, it all happens behind the scenes, without ever interrupting the learning experience.
AI transforms learning into a competitive advantage
A survey of 3,000 business leaders by Boston Consulting Group and MIT Sloan Management Review found that almost 85% of executives believe that AI will allow their companies to sustain a competitive advantage. But due to the limitless potential of Artificial Intelligence, how to obtain that advantage in L&D has remained unclear. So, let’s look at a few (of the many) functions that AI is currently capable of accomplishing in L&D:
Auto-tagging – This function “listens” to content assets and learns to understand various keywords within files, to produce tags that categorises content. The best part is that AI does it all automatically, saving a huge amount of time for the admin. If content is ever updated, AI would continue to crawl that asset and update its tags, if necessary.
AI powered deep search – This AI functionality takes content discovery to a new level by deeply analysing learning assets and the way they are used in the organisation. This helps to improve sharing of both traditional and user-contributed learning assets, no matter the format.
Upon sharing new content or creating a new learning object, AI examines it entirely and it’s keywords to produce search results that are more relevant.
Invite to watch – It’s designed to elevate the social learning experience by ensuring that relevant learning assets are recommended to learners who value it most. When you upload a piece of content to your learning platform, AI (machine learning in this case) analyses it to produce a list of learners who have shown an interest in similar assets in the past.
From an automation perspective, these three functionalities come together to improve the way your admins and learners interact with the content.
AI in L&D is much more than a content suggestion engine
When most people are asked how they think AI has impacted our everyday lives, the go-to response is the content recommendations offered by Netflix or Amazon. Indeed, AI is the backbone of these suggestion engines, but it’s so easy to limit a learning platform’s AI functionalities to just that: a suggestion engine.
AI-powered learning is much more than that. It opens up new capabilities for admins to develop immersive and personalised learning experiences, while automating menial tasks.
Automate content scheduling and delivery
What would be the point in adopting new technology if it didn’t help us out? An AI-powered learning platform has the ability to schedule coursework and deliver resources based on an individual learner’s assessment results. This helps build an environment in which it’s possible to automatically predict a learning path for each of your learners who enrol on any of your courses.
Personalised learning at scale
Delivering the same form of content to every single learner is a very common approach in corporate training. But learners want to learn on their own terms and have great expectations for flexibility – they want to learn anytime, anywhere. AI ensures personalised experiences for each of your learners by analysing their behaviour, what courses they’ve interacted with most, and their preferred format for learning.
This information is then used to understand what’s most relevant to a learner. For example, learners that express a particular skills gap receive targeted recommendations that help build their knowledge in that particular skill. This could include situations where the system would recognise that a learner might be able to skip a few modules to take a more comprehensive and less linear learning journey compared to someone who might lack the basic skills related to that particular topic.
Like fine wine, AI gets better over time
AI requires regular data injections to be most effective. Think of AI as a learner itself: the more data they consume, the more intelligent they become. Using different types of data, AI needs to be exposed to as many variables to complete a task as possible. Some AI systems create their own tasks after they’ve identified the goals for the data they’ve been fed.
In the context of digital learning, the effectiveness of an auto-tagging functionality depends on a consistent stream of data, to become more useful and valuable to learners over time. This is particularly helpful when it comes to enabling learning in the flow of work, where the learner is seeking out an answer to a specific question. Over time, as AI is fed with more and more tags, which are also editable by humans, the functionality becomes more effective, enabling a continuous improvement cycle that requires no human intervention.
The application of AI in L&D is not just a cost-saving solution; it also opens up a whole new way of looking at learning itself. AI has the potential to emphasise areas of improvement for individual learners to create immersive learning experiences – not just lessons. I truly believe AI will soon become the beating heart of your L&D strategy, the fuel you need to drive your learning efforts and your workforce into the future.