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Afterword: Learning in the Age of Algorithms
Abstract
This chapter looks across the landscape of learning in the current age of algorithms and so-called ‘artificial intelligence' with a focus on issues raised in the concept of “the master algorithm” around learning models and the future of learning. Pedro Domingos identifies five “scientific” theories of learning algorithms and presents them sequentially and so capable of improvement by the theorist (and he alone). By contrast, in her conversational framework, Diana Laurillard presents four approaches to framing learning models. The authors prefer Laurillard's modelling but believe the fifth dimension of rhizomatic learning needs to be added to her framework in order to enable the learner to take the final decisions on what has been learned and what they will do subsequently, and so produce a learner-centric framework for learning and architectures of participation. They examine several histories of thinking about intelligence as well as long-term views of technology before outlining, briefly, a phenomenonology of learning as the potential countervailing ideas to AI in education.
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