AI in eLearning; now and the future
When it comes to discussions about technology, there’s always a lot of talk about AI (Artificial Intelligence) and ML (Machine Learning, a subset of AI) and the many ways they can be used in the applications we interact with.
With the ever-increasing amount of data that’s collected from various systems, AI can analyse data and help us perform many different tasks faster than ever before.
AI is already being used in numerous different fields. In some cases, systems are provided data to analyse and feedback information, such as in healthcare, where AI is starting to be used to see how it could detect a range of issues based on a quick snap from your phone. By switching your holiday selfie to a picture of a rash, systems can analyse and compare a huge database of skin related issues to detect any possible problems all before visiting your doctor, and as with lots of AI systems, the more data they are provided, the better they get.
In other cases, the data provided is a series of rules and guides, which allows an AI-driven system to complete time-consuming tasks. In the world of computer-generated special effects, for example, the cost of designing and building 3D environments for a superhero to smash through in a bid to save the world is huge. An entire city might have to be built for them to fly around in, and building a city, even a virtual one, is expensive. With AI you can develop a system to build the city for you by providing data and rules on how to design a building, such as each house should have at least one door (and that the door should be on ground level… pretty important), the minimum number of windows it should have and so on. With this data and rules, AI can design hundreds of buildings in no time.
Now and the future of AI in eLearning
AI advancements are happening everywhere, and eLearning is no exception. So, here are three ways we can see AI benefiting learners and (let’s not forget) the learning development admin team.
- Personalised recommended content
We’re starting with this as it’s perhaps the one that everyone has experienced at least once before via an online shop or video streaming service. The idea is simple; based on what you have been browsing or buying recently, new product recommendations are made to you. This will be determined by other users’ browsing, watching or purchasing history – so if other users view content A and B, and then you view content A, content B is recommended to you.
In eLearning platforms, if you have a general content library, or even a social learning element, this kind of function can be really useful; providing content suggestions that have been personalised to the individual, which grows and changes as they and other users access your system.
- Content creation support
Building eLearning content is a time-consuming process, but there are many elements where some AI could support. For example, systems can read and understand paragraphs of text and then, combining this with image recognition, search an image library to automatically find a suitable image for your content. This system could also adapt to your preferences, as you accept or reject each suggested image, learning what type of image you prefer to help you find the perfect picture in seconds.
Automated tagging, grouping and linking of content is also possible. When you have a large content library and you are just one of many contributors, it can be tricky to be aware of all the other content in your system and how one piece may relate to another. By allowing systems to process your content, it can understand the text, then suggest keywords and tags for your content to make it easier for users to find. An AI system can then relate one piece of content to another, so when a user has finished reading your content, new related content suggestions are made, which are automatically updated and adapt as new content is added to the system.
- Personal adaptive tutoring
Imagine if, when learning a new subject, a teacher would bring up further examples of topics you didn’t understand first time. Or only provide high-level information for areas you knew well. Or switch to more image-based content for more visual learners. This would surely make learning easier, but in a real classroom setting these options aren’t always possible as you have to teach to the ‘average’ learner.
In a digitally-led world, a more personalised experience is possible. The system gets better, the more students it teaches. AI systems collect data on what information has been provided to the learner and analyses their test results. It can adapt the type, level and the best way to present content to the learner based on their previous interactions and those of other learners. This enables a learning profile to be developed; matching you to one of these profiles and providing the best learning experience.
This type of learning requires a particular approach of a continuous ‘teach and test’ cycle, which may not be suited to all types of training but is for some forms of adaptive learning experiences. Language teaching apps are a perfect example; they raise or lower the difficultly based on every interaction you make, providing a much more flexible learning experience that matches your learning speed, and in turn increases your speed to learn.
Growing importance of AI
AI will continue to grow in importance in many aspects of industries. Understanding what’s possible and how it can be used in your field will be key. Being able to utilise these technologies to make work and life easier, more flexible, personal, faster or cheaper, and is going to be required to give yourself and your company an edge over the competition.
Intrigued? To find out more about developing people and transforming business in a digital age, please join us on 12th September for our event. Places are limited, so if you'd like to find out more and register, please visit the Eventbrite page.
If you'd like to see how some of these elements have been incorporated into our learning experience platform, please get in touch for a demonstration.