At the heart of AI in Education (AIEd) is the scientific goal to “make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.” In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us deeper, and more fine-grained understandings of how learning actually happens (for example, how it is influenced by the learner’s socio-economic and physical context, or by technology). These understandings may then be applied to the development of future AIEd software and, importantly, can also inform approaches to learning that do not involve technology.
For example, AIEd can help us see and understand the micro-steps that learners go through in learning, or the common misconceptions that arise. These understandings can then be used to good effect by classroom teachers. AI involves computer software that has been programmed to interact with the world in ways normally requiring human intelligence. This means that AI depends both on knowledge about the world, and algorithms to intelligently process that knowledge.
This knowledge about the world is represented in so called ‘models’. There are three key models at the heart of any AIEd application: the pedagogical model, the domain model, and the learner model. Take the example of an AIEd system that is designed to provide appropriate individualised feedback to a student. Achieving this requires that the AIEd system knows something about:
• Effective approaches to teaching (which is represented in a pedagogical model);
• The subject being learned (represented in the domain model);
• The student (represented in the learner model);
In my next post I’ll provide examples of the sorts of information that can be found in each of these models.
This post is an adapted extract from Intelligence Unleashed published by Pearson.