logo image

ATD Blog

What Exactly Does Adaptive Learning Look Like? Part 2


Thu Oct 19 2017

What Exactly Does Adaptive Learning Look Like? Part 2

ATD product manager Jeffrey Surprenant sat down with subject matter expert James Bennet to uncover not only what adaptive learning is and looks like, but also the why and how behind implementing it in your organization. Read part one of their conversation.

Why Would an Organization Want to Implement Adaptive Learning? What Are the Benefits?

James: The really obvious benefit from an organization’s perspective is the reduction in revenue loss—and that is something that you can take to the accountants when it comes to pitching adaptive learning.


I am going to give a very simplified example, but it will illustrate the dramatic impact of this one benefit: It is often common to think about training in terms of “man hours” from the payroll. In other words, if a company has 200 employees, making an average dollar figure per hour, it is easy to come up with a dollar amount to assign to an hour’s worth of training for every employee. But looking at it from the perspective of revenue, the question becomes, how much money does the entire company generate in an hour? If adaptive learning reduces the average time each employee spends on training by 25 percent, then that is the amount of revenue-generating productivity regained.

That is one benefit, but there are certainly others.

When detailed and accurate data around the learning that is taking place is needed, adaptive learning really excels. The adaptive learning system provides accurate evidence of what learning has actually taken place. It is not only able to track the single learner, but it can do data analysis, measure trends, and even help make informed company decisions based on what the systems can tell you about employees and staff. In essence, think of all the recent advances companies make using big data—adaptive learning can allow forward-thinking organizations to turn the analysis telescope around and look internally, as well as externally. That is a pretty big deal.

Of course there are several more, but for the sake of brevity let’s stick with these two really big ones.

What Challenges Need to Be Addressed Before Trying to Implement an Adaptive Learning Program?

James: The biggest challenges I have seen with adaptive learning are really around misconceptions people have. They think it is too expensive, or to fancy, or too “mystical” without really doing the research in savings, simplicity, or evidenced benefits.


Do I Need Special Tools To Develop Adaptive Learning Courses?

James: Yes, you are going to need to build courses in an adaptive learning system that supplies the artificial intelligence (AI), but that isn’t difficult. It isn’t any harder than building something in a learning management system (LMS) or a typical web content management system. In fact, many systems actually fill the LMS role as well. That being said, there are a lot of solutions to choose from, and one can be found that fits the needs of any organization—different billing and business models, and different systems that range from the smallest learning experiences to entire programs. The key is to take the time to find the one that best fits your needs.

What Considerations Do I Need To Give When Designing An Adaptive Learning Course?

The key for designing an effective adaptive course is to ensure each component really covers what it is supposed to. Is each learning objective really what you want the learner to come away with? Does the content really address the learning objective, not just something related to the learning objective? Does the assessment really measure the attainment of the learning objective?

In other learning deliveries, although we shouldn’t, we can sometimes get by with a little looseness—especially when there is a human involved in the teaching process. But in the case of adaptive learning, we have AI making determinations that are dependent on the information being fed into it. The instructional designer needs to carefully consider how each piece fits and if it is truly doing what it is supposed to. For example, say you have a learning objective that requires learners to be able to identify and use the different functions of a software application, but the assessments focus only on identifying and naming the different applications. In such a case, we would be asking AI to make decisions based on information that was not a true measurement of what was required. Computers do not do very well with ambiguity.

Learn more about ATD Elements, our newly launched self-paced learning library powered by ATD’s adaptive learning engine.

You've Reached ATD Member-only Content

Become an ATD member to continue

Already a member?Sign In


Copyright © 2024 ATD

ASTD changed its name to ATD to meet the growing needs of a dynamic, global profession.

Terms of UsePrivacy NoticeCookie Policy