Modern Training

Personalized vs Adaptive Learning

Posted on: September 25, 2015Updated on: June 20, 2023By: Jen Buchanan, VP of Product Management

What’s the difference and do you need both?

We get a lot of questions about the difference between personalized and adaptive learning. This makes sense because they’ve often been used interchangeably in the learning realm. I’m embarrassed to say that I too have been guilty of substituting one term for the other from time to time.

So, here’s the lowdown on what each term means, how they’re different and how they work together in a modern learning scenario.


Each employee follows their unique path. (Img source:

What’s the Difference?

The main difference between personalized and adaptive learning is that adaptive learning is optimized based on the learner’s behavior and progress.

Personalized Learning provides employees with different learning paths, which are programmed by L&D according to:

  • Job function and department.
  • Specific knowledge required for the job.
  • Prior learning and/or test results to benchmark current knowledge levels.
  • Personal attributes such as primary language, age group, seniority level.
  • Appropriate methods of instruction such as classroom, eLearning, coaching etc.

Employees take an initial test to identify their baseline knowledge levels. Then, content is prepared in advance and identified for the learning system to serve up in a prescribed manner. As the employee takes training and achieves a certain test score result, new learning modules are opened up based on the defined learning path. Learning progresses in a largely linear method.

Adaptive Learning, on the other hand, takes into account all the characteristics of personalized learning (such as job function, knowledge requirements, current knowledge levels and more) but uses a sophisticated, data driven and often non-linear algorithm that:

  • Continuously evaluates information from the learner during learning sessions (such as test answers, topics re-taken, and knowledge confidence levels, if tracked).
  • Compares the learner data to initial benchmark knowledge levels, plus target knowledge levels, programmed into the system.
  • Adapts the learning path automatically with modifications being made on the fly to subject matter, level of difficulty, learning resources and even methods of knowledge delivery.

Do You Need Both?

The answer is “Yes!” Here’s how personalized and adaptive learning come together in a real-life scenario.

Personalized learning establishes the starting point

Ed and Jeremy join Widgetco as forklift operators in the warehousing operations division. Their job function dictates that they require knowledge and skills in subject matter, such as:

  • Safe forklift operation
  • Warehouse layout
  • Using the warehouse inventory system

Ed and Jeremy take some quick online tests to assess their current knowledge levels. Their scores indicate they both must start at Level 1. Both complete their learning and take a final test. Ed finishes with a 57% knowledge level and Jeremy ends with a 95% knowledge level. Because the mandatory score to “pass” is 90%, Jeremy moves on to other learning, while Ed must re-take the training.

While Ed could take the course a second time and re-test, this approach isn’t very efficient. The learning path is the same as the first time he took the training and doesn’t allow him to focus on the areas where he is having the most difficulty. This is where an adaptive learning program can help.

Adaptive learning ensures subject mastery across the board

In an adaptive learning environment, the eLearning system identifies which topics present challenges for Ed, and which subjects he is strong in and able to achieve the 90% knowledge level. The system presents Ed with additional learning on the challenging topics, first at an easy level. As his knowledge begins to improve, the system continues to evaluate his knowledge levels and present him with information at increasing difficulty levels, placing more emphasis on areas he’s weak in, and less emphasis on stronger areas. The system continues to review his response to questions, until he achieves subject mastery at the 90% knowledge level.

Personalized and adaptive learning: a powerful combination

As we described above, personalized and adaptive learning actually address employee learning at different points in a learner’s journey. We believe that to create the most powerful and effective learning, personalized and adaptive learning must work hand in hand:

  • Personalized learning establishes what the learner must learn.
  • Adaptive learning helps the learner successfully achieve target knowledge levels, modifying learning content to identify knowledge gaps, then focussing on those areas until the learner demonstrates subject mastery.

The result? Learning that meets the individual needs of modern learners and allows them to increase their knowledge more efficiently and effectively.

Jen Buchanan, VP of Product Management

Jenn is one of those lucky people who works in the sweet spot between product and marketing. She has the rare pleasure of working with all aspects of the business, ensuring teams have a deep understanding of the problems we are solving.