Here is what ‘smart’ looks like in an AI tutor


So, what would a piece of education technology driven by AIEd look like? Here is a simplified picture of a typical model-based adaptive tutor.



It is based on the three core models as described above: the learner model (knowledge of the individual learner), the pedagogy model (knowledge of teaching), and the domain model (knowledge of the subject being learned and the relationships between the different parts of that subject matter). AIEd algorithms (implemented in the system’s computer code) process that knowledge to select the most appropriate content to be delivered to the learner, according to their individual capabilities and needs.

While this content (which might take the form of text, sound, activity, video, or animation) is being delivered to the learner, continuous analysis of the learner’s interactions (for example, their current actions and answers, their past achievements, and their current affective state) informs the delivery of feedback (for example, hints and guidance), to help them progress through the content they are learning. Deep analysis of the student’s interactions is also used to update the learner model; more accurate estimates of the student’s current state (their understanding and motivation, for example) ensures that each student’s learning experience is tailored to their capabilities and needs, and effectively supports their learning.

Some systems include so-called Open Learner Models, which present the outcomes of the analysis back to the learners and teachers. These outcomes might include valuable information about the learner’s achievements, their affective state, or any misconceptions that they held. This can help teachers understand their students’ approach to learning, and allows them to shape future learning experiences appropriately. For the learners, Open Learner Models can help motivate them by enabling them to track their own progress, and can also encourage them to reflect on their learning.

One of the advantages of adaptive AIEd systems is that they typically gather large amounts of data, which, in a virtuous circle, can then be computed to dynamically improve the pedagogy and domain models. This process helps inform new ways to provide more efficient, personalised, and contextualised support, while also testing and refining our understanding of the processes of teaching and learning.

In addition to the learner, pedagogical, and domain models, AIEd researchers have also developed models that represent the social, emotional, and meta-cognitive aspects of learning. This allows AIEd systems to accommodate the full range of factors that influence learning. Taken together, this set of increasingly rich AIEd models might become the field’s greatest contribution to learning.



This post is an adapted extract from Intelligence Unleashed published by Pearson.


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