A category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes.
The idea of personalized learning (sometimes referred to as adaptive learning or differentiated learning) is by no means new. In fact, in the physical world this simply means a one-to-one tutorship between teacher and student. However, this model is not practical nor is it cost effective. What if a ‘system’ could perform a similar task? What if a ‘system’ could understand the learner, recognize where they are failing to grasp a concept and knowing all possible learning options can direct their learning pathway accordingly?
In a one-to-one setting, this is meat and drink to an experienced teacher as they draw on their years of experience and skills to explain topics in a variety of ways. But in a class of 30 students, there is a wide array of abilities and there are simply too many variables and too little time for a personalized approach. Invariably, a ‘personalization proxy’ takes place whereby the teacher differentiates student abilities along a bell curve, effectively teaching to 3 or 4 cohorts of varying aptitudes. This is not ideal, and the problem is further exacerbated because learning is sequential. If a student fails to ‘get’ algebra 101, there is little hope they will ever come to terms with simultaneous equations. What if a cognitive system could support a teacher to prevent such learning roadblocks for each and every child?