As we have discussed in previous posts, there are several different ways to personalize learning. Some AI applications generate personalized learning paths based on learner performance and background data such as job role. Other AI platforms make content suggestions based on the learner’s actions in a course. In both cases the course developer would create the algorithms that would show or recommend Y when the user did X.
Personalized learning paths by themselves provide only a limited degree of personalization when the path leads to linear courses that are not adaptive. AI systems that use the “every learner that does X gets Y” algorithms also lead to non-adaptive content. Getting a question wrong will trigger the same feedback and suggestion for all learners, unless additional data is being considered. The degree of AI personalization depends on the quantity and complexity of the algorithms. The more detailed the tracking and the more alternatives offered the greater the degree of personalization.
The Learner Intelligence (LI) approach is an adaptive design model that gives each learner the ability to personalize the learning experience at any time. Getting a question wrong will trigger the same feedback for every learner, but each learner can respond differently based on personal choice. The degree of LI personalization is a factor of the number of choices. There are two big differences that makes it easier to create personalized learning with LI.
Designer Control
The big advantage of the LI Adaptive Design Model is that the course developer uses familiar design tools and methods. Thus, it is much easier and quicker for a designer to add adaptive features using LI than it is to program, test, and debug AI algorithms. This may change as AI personalization evolves, but for now a higher level of adaptability and personalization is available from an LI design.
Real-Time Adaptability
The other big difference is that LI does not have the built-in latency that exists with AI. AI is based on past user performance which creates a lag before the course can adapt to the learner. On the other hand, an LI course provides a personalization menu of options that adapts learning at any time with zero lag. This is perfectly suited to the modern learner who frequently learns while connected with others using social tools. The next post will take a deep dive into Learning in a Social World.