There are different approaches to personalizing learning. One of the simplest approaches used by some AI platforms is to provide a recommendation engine. Courses are recommended based on the learner’s past performance with other courses. This is similar to the way Amazon and Netflix offer suggestions. Even though the courses may not be adaptive, each learner can follow a personalized learning path.
Nick Howe of Area9 Lyceum was explaining how their AI system tracks and analyzes the learner’s behavior in a course. Knowledge checks are used to test understanding of the subject. This is followed by a confidence check, asking how confident the learner is that their answer is correct. This rules out lucky guesses which would give an inaccurate assessment of the learner’s progress. Based on these responses, the system then suggests different content to the learner. In this way each course can be personalized to the user.
Docebo is developing a process of tagging various forms of content such as video and audio. The user will then be able to search and locate relevant content from a variety of learning assets. In this way the learner can co-create a highly personalized course. It also gives the learner a level of autonomy not typically found in AI enhanced learning environments.
The LI Adaptive Design Model
It is important to note that AI is not required for personalized learning. Where AI makes suggestions based on the learner’s past performance, Learner Intelligence (LI) provides real-time choices based on the learner’s current performance. This is important because learner’s do not have a static profile that can be used as a basis for predictive learning.
During the learning process a learner’s needs are fluid. If you think in terms of learning styles, an avid reader can at any time prefer to watch a video or engage in an interaction. In terms of job role, a salesperson may want to learn more about the technical details. The LI Adaptive Design Model is ideally suited to having options ever present, giving the learner the ability to personalize instruction in real-time at the point of need. Next week we’ll explore some of the Advantages of the LI Adaptive Design Model.