The media has written extensively about artificial intelligence (AI), fretting about how it will replace humans in almost every job and be the demise of human civilization. But on a more positive note, we have gained a lot from machines and “AI-augmented” humans, from sensors to prosthetics to gene editing. Little attention, though, has been given to the more modest, but potentially impactful, knowledge transfer from machines to humans: teaching, learning, and assessment in schools.
We are referring to the deconstruction of complex human behaviour into educational strategies that teachers can deploy in their classrooms. Machines can study and measure behaviour in a way that the contributing cognitive and social processes that are part of children’s behaviour are identifiable.
Here we consider three examples where computational psychometric models have identified successful strategies for solving problems in digital environments: 1) the analysis of eye-tracking data to improve the development of learning environments by human experts; 2) the analysis of “chat data” from collaborative problem-solving tasks to provide learners and teachers with the most efficient strategies for successful collaboration; and 3) the analysis of sequences of learning behaviour to identify hurdles that may lead to dropping out of school in order to identify the optimal point for feedback and encouragement.
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