Article Summary: Enable Expert Performance with Adaptive Learning by Michael J. Noble

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People are in awe of excellence, whether that’s in sports, music, mathematics or business. But, adaptive learning expert Michael J. Noble argues, most people achieve excellence through hard work and instruction. At the same time, he notes, few would assert that corporate learning yields high-end excellence. Even the most recent advances in digital learning tend only to result in greater, more equitable access. Learning of this kind is rarely transformational. By contrast, adaptive learning, aided by artificial intelligence, is tailored to individuals’ specific needs – and is a game changer.

Take-Aways

• Adaptive learning uses AI technology to tailor learning to individuals’ needs.
• Adaptive learning differs from e-learning in important ways.
• With adaptive learning, learning and data analytics shape design.
• Future generations of adaptive learning approaches will be even more ambitious at every level.

Summary

Adaptive learning uses AI technology to tailor learning to individuals’ needs.

Learning, whether it leads to mastery or competence, involves devotion, hard work over time, and, most likely, some form of teacher or tutor. Over the past two decades, digital learning has made significant advances, but few believe such advances lead to exceptional mastery – they just tend to provide broader access to a wider range of people. Business or corporate learning professionals aim to provide learning content that is at least somewhat engaging and to ensure high rates of successful course completion, but these standards rarely lead to more than serviceable knowledge. In the end, most digital learning isn’t all that different from traditional classroom learning.

“Transformation calls for something else, something more than minimally prepared knowledge workers. It calls for mastery and higher levels of expert performance.”

Adaptive learning doesn’t indulge the concept of “learning styles” and responds to learner performance in real time, rather than personalizing content in preset ways, such as displaying or hiding information based on the learner’s job title. Adaptive learning incorporates both scientific research and artificial intelligence (AI) into the learning process. Artificial intelligence analyzes people’s data and adapts learning to their specific needs. It can act like a personal teacher but can do so on a massive scale. It can, among other things, coordinate learning needs with available courses, organize learning paths based on how well someone is doing at a given stage, and regulate learning protocols and habits to optimize success.

Adaptive learning differs from e-learning in important ways.

In general, e-learning provides learners with a one-size-fits-all learning model. It is grounded in preset content and proceeds linearly. Students merely need to click through the information, paying minimal attention, to be able to pass any end-of-course summary assessment of the content.

“[When learners] encounter an adaptive solution for the first time, they may feel like they’re being tested. They may be worried about failing.”

Those used to e-learning often find the shift to adaptive learning to be challenging: Adaptive learning demands more engagement. It pinpoints – and then immediately works to address – learning gaps in the midst of the learning process. Learners need to see how the adaptive approach gives them more in terms of mastering and remembering material than traditional learning, or they may become frustrated with the extra effort required.

With adaptive learning, learning and data analytics shape design.

Artificial intelligence didn’t create adaptive learning, but it made it possible to deploy an adaptive learning model on a large scale.

“With adaptive learning, the ability to target individual learners supports the best of learning science. It should also help us to evaluate and measure effectiveness.”

The amount of data collected through the process of adaptive learning, via AI, is remarkable, and the data itself can shape the design. For instance, rather than having to retire an out-of-date course, L&D professionals can use learning and content analytics to continuously update the course to meet people’s needs.

Future generations of adaptive learning approaches will be even more ambitious at every level.

Future iterations of adaptive learning will be even more expansive and ambitious than current ones. They will, for one, be able to incorporate lessons from all the data collected through AI. Future adaptive learning approaches will be applicable to entirely new demands for knowledge and skill. They will inevitably contribute to business aims. But they will also contribute to humanity’s broader aspiration to learn.

About the Author

Michael J. Noble, PhD, is the president (Americas) of Area9 Lyceum.