HR Management & Compliance, Technology

How to Use AI to Drive a Great Experience for Your Learners (Part 1)

Implementing Artificial Intelligence (AI) is about much more than finding a better and more efficient way to deliver the most relevant learning content to your learners at all times. If implemented properly, AI should also deliver engaging and lasting learning experiences for your learners that will keep them more engaged and productive at work.

Understanding AI in E-Learning

AI in e-learning is typically carried out via machine learning implemented in a Learning Management System (LMS). Machine learning in an LMS is carried out by algorithms that are included to predict possible outcomes based on user and learner data. It does so on an automatic basis as it continuously gleans information from the data it receives. So, as you and your learners add data to your LMS, it learns about each learner and begins to offer them more personalized learning experiences.

Research Available and Applicable AI Tools

If you’re interested in implementing AI, know what AI capabilities your current LMS has, as well as what types of integrations it has available. And know what types of AI algorithms it supports and executes. While some LMS already have AI features built in and don’t require any programming, others will. And if your LMS doesn’t have any built-in AI capabilities or algorithms, you’ll want to consider third-party applications or vendors that can integrate with your LMS. You may also need to consider third-party options if you don’t have resources to program algorithms for you.

Know Your Machine Learning Options

When implementing machine learning in your LMS, there are three different types of algorithms you can use.

1. Supervised

A programmer must provide the system with inputs and outputs to train the software. The system uses past examples and new data sets to predict the outcomes and, over time, will automatically construct outputs or targets for new data sets.

2. Unsupervised

The system evaluates data to identify patterns and make extrapolations or predictions. It’s not a matter of mapping the input to an output but detecting more obscure trends or insights in data sets.

3. Reinforcement

This algorithm includes a specific task or goal that the system must complete. It receives feedback to learn the desired behaviors, and desired behaviors are reinforced.

Be Realistic and Build an AI Strategy that Will Work with What You Have

Overall, when using AI to enhance your learners’ experiences, always remember the tools you already have to work with and what algorithms you’re implementing when you think of your broader AI-enhanced learning strategy. For example, you won’t be able to predict what learning content will be most valuable to a group of your learners if you don’t have the tools to execute the algorithm that’s needed to do so. And you won’t be able to offer personalized learning content and experiences to your learners if the right algorithms aren’t set in place. And so on.
(In tomorrow’s post, we’ll include more ways to use AI to drive a great experience for your learners.)