If there’s one trait that has defined L&D ever since the advent of digital learning environments, it’s adaptability. Not only is the technology available to Learning Management System (LMS) creators constantly changing, the technological proficiency and expectations of the learners in the system are constantly evolving with the tech too.
Today’s eLearning systems are very different from the ones that were used pervasively even a few years ago, and it’s almost certain the ones in use five years from now will have revolutionary features that today’s platforms don’t anticipate. One trend that is just coming into its own within the L&D sphere is the use of machine learning and artificial intelligence. It’s not just an emerging technology that will inform course design, it’s also a resource that can be relied upon in today’s more sophisticated LMS platforms.
How AI Works in Learning Environments
Before launching into the functions that LMS designers can use AI to accomplish, it helps to make sure everyone is on the same page about what the term means. For a lot of people, the phrase conjures up images of fantastic machines that are basically living organisms unto themselves, just constructed ones. Unfortunately, that’s far from the reality.
Today’s AI systems are simply designed compared to the fictional alternative, but they do make decisions based on a combination of incoming data and the results of past decisions. That’s where the intelligence part comes in. The process by which they gather the requisite experience to make quality decisions about what to serve a user is called machine learning, and you can see it in place in everyday applications like search engines.
When using artificial intelligence in an LMS, those decisions can guide the content the user sees and the form of the evaluations used to track progress, allowing for personalized learning paths that are more suited to the individual than the ones designers could create in past systems. There are three ways this background AI functions to improve learning and the user experience.
- Curation of content/courses presented to the user from the total database of available content
- Judging the relevance of the content being served to improve curation
- Providing insights about user behavior to both the learners and instructors/designers
These three functions blend seamlessly in the background of flexible learning management systems, so the learner often doesn’t realize how much work they do. Let’s take a look at each one.
There are a number of ways that you can judge whether the content being served to a user is relevant. Search engines use factors like time spent on a website, number of pages visited, and whether users link back to that site from other locations. Social media sites make use of interaction data, including shares, likes, and comments.
When it comes to LMS design, both approaches provide useful data, and that data becomes even more useful when it is combined in the decision-making process. This gives weight to whether a learner puts time into content as well as whether it is useful enough to share or start a discussion around.
As the user interacts with the system more and more, the past history of content interactions and pages read are used to determine the most relevant information to serve next, giving the user resources that are more focused on the aspects of the content that seem most useful in that case. By serving more useful content, the system also encourages further interaction, reinforcing the positive effect of continuous learning while encouraging users to keep coming back for more.
It might seem like judging relevance and curating content are the same thing, but they are not. While they happen together as part of the process of delivering customized information to users, curation goes further, taking into account organizational goals, the learning objectives of the course at hand, and the role of the learner. This, along with the curation of relevant content, provides the focus necessary to steer the learner into the skill acquisition the course is designed to facilitate overall.
AI curation also allows for a meta-analysis of relevance judgments based on classes of user data to better target demographics of user, like middle managers or department heads.
In many ways, this topic was already folded into the last two, but there’s more to insights than just spotting trends and curating content. They can also be used to encourage regular system use by serving information about how the individual learner uses the resources in the LMS and how that compares to the average user. This lets people know if they are checking in less frequently than the most successful learners on the platform tend to check in, as well as whether they are over-emphasizing or under-emphasizing aspects of the course material as they work through it.
These three components are hard to separate from one another because they happen in a fluid process of giving feedback, observing behavior, and serving content that happens recursively and without a rigid order to those steps. When they’re at work, artificial intelligence just looks like the platform working, so it can be easy to miss for end users.
Incorporating AI into your Learning Platform
The modern learner wants hyper-relevant eLearning content that can be recommended to them when they might need or want it. AI is an incredibly powerful tool that can help learning managers deliver this type of personalized experience. Now, how exactly does one incorporate AI into their learning platform?
Using a Learning Experience Platform (LXP), various types of content from Subject Matter Experts (SME) and most importantly, a flexible LMS, you can easily create a curated learning experience through AI.
First, gather content from multiple sources internally, externally, formally, and informally to create a library of learning resources related to your industry. Next, integrate your LMS with an LXP, which can be essentially layered on top of an organization’s existing LMS to provide a Netflix-like experience within the LMS to suggest content based on employee role, skill level, activities, previously completed courses, or more. An LXP can aggregate learning content from multiple sources like Lynda.com, Skillsoft, or even YouTube and make recommendations based off of data associated with the learner using machine learning and AI. Whereas learning in an LMS is often driven by instructors, department managers or administrators, learning in an LXP is largely driven by the learners themselves. Learners can add content to their training plan and decide which content they want to consume, as well as how and when they want to consume it.
Ultimately, this allows learners to easily access courses and resources that directly impact their performance or help them achieve a certain skill level, creating a more effective and engaging training solution.
When it comes to LXP options eThink performs a needs analysis and then recommends the best fit in the market. Leaders in the space include EdCast and Degreed, and Sana Labs is an interesting new addition to the AI market. For more information, check out this guest blog post by our friends at EdCast: A Recipe for Modern Learning: Ingredients for a Deliciously Effective Learning Ecosystem.
Or, contact eThink or request an individual demonstration below to explore utilizing artificial intelligence in your eLearning solution.
Want to learn more about the impact of Artificial Intelligence in eLearning? Explore how to navigate the content learning landscape with AI in this webinar!