Application of ML and AI in eLearning

In our article about the trends in IT, we have already touched upon the topic of artificial intelligence and machine learning going mainstream in IT healthcare. Also we have discussed how ML and AI could improve the healthcare industry in the future. 

However, healthcare is not the only industry where ML and AI have huge potential. Deeply rooted in data science, it has countless applications depending on the specifics of your product or service you are delivering. Not so long ago we mentioned its application in eLearning, and today SOLVVE would like to guide you deeper into the world of ML and AI solutions in online education.

But first, let us sort out what is the connection between data science, artificial intelligence, and machine learning.

What is the difference?

Artificial intelligence or AI has become a buzzword in recent years. When we talk about AI we broadly talk about technologies that can do specific tasks to solve predefined problems. To put it simply, we talk about specific algorithms that perform specific assignments way more efficiently and faster than a human could.

However, let us take one step further. We can train these algorithms of artificial intelligence to “make decisions”. And the way to train them is called machine learning or ML for short. The more AI works with similar algorithms, the better decisions it can make by refining the results and re-training itself. If you have ever received a recommendation of a person you might know on a social platform or got a very nice movie suggestion from Netflix, you already know how accurate these algorithms can be.

The more information you feed the better outcomes you would expect. Where can we get enough information grouped in batches by a certain common feature to train our algorithms? Data science is the answer. It provides researchers and developers with data sets needed for training.

To sum up, we need big data to train artificial intelligence and this process we call machine learning.

Trends involving ML and AI in eLearning

To find more in-depth information we recommend reading our dedicated article about the topic. However, we cannot stress enough that that big data will become more and more important to eLearning in the future.

On the one hand, eLearning is mainly student-centered and highly-personalized creating perfect opportunities to collect very specific information about individual learning processes, progress, and experience. On the other hand, the very same information can be used to improve the learning outcomes. 

And there is no sign of this trend slowing down. Business Wire reports that “The global eLearning market is fast-growing and expected to reach $398 billion come 2026. As such, to remain competitive in the industry, you need to adjust to every trend in order to improve your training.” 

Thus, let us take a look at what has already been done using ML and AI in eLearning.

Available solutions for AI and ML in eLearning

One of the simplest algorithms that you could encounter is spaced learning or spaced repetition that is often used in language learning apps. Your app adapts to your learning and keeps on providing you the words that you need to repeat so that you do not forget them.

The second option is to employ AI as a teacher or a mentor available anytime anywhere. For example, one could use a chatbot to address students’ questions and at the same time collect information on what kind of issues students encounter. Already today chatbots are easy to create and affordable to continuously maintain.

Thirdly, AI could help in making customized learning plans. As was already mentioned above, eLearning could collect data on particular students to improve their performance and provide them with better training. Iterating on this loop could bring highly-customized learning experience of any complexity to students.

Moreover, one could generate, refresh, or repurpose the learning content. A lot of educators’ time is dedicated to creating learning materials and compiling courses that can only be used so often and so much. Instead of engaging with their students more to improve their experience and outcomes, educators have to spend a lot of time working through the content trying to keep it up to date and engaging. Artificial intelligence could help drawing insights from students’ work and help with rearranging course materials or even repurposing previously used ones.

Natural language processing, including voice recognition, speech-to-text transformation, and automated translations, can solve such issues as teaching to students speaking different mother tongues or students that might need special assistance in their studies due to health conditions. The employment of natural language processing holds great potential to make eLearning accessible for a wider audience.

Summary

As we have seen in this article, there are many ways in which ML and AI can facilitate eLearning. Currently, many of these applications have their limitations. Nevertheless, this technology improves fast, and adopting it early can significantly benefit businesses later on. 

If you have any questions or ideas related to data science, machine learning, or artificial intelligence applications in your eLearning projects, do not hesitate to contact us. Let us make this happen!