Browsing by Author "Martinez-Maldonado R"

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    How can we design for learning in an AI world?
    (Elsevier Ltd, 10/02/2022) Carvalho L; Martinez-Maldonado R; Tsai Y-S; Markauskaite L; De Laat M
    Fast improvements in computing power and Artificial Intelligence (AI) algorithms enable us to automate important decisions that shape our everyday lives, and drive workplace transformations. It is predicted that many people will find themselves unprepared to deal with high degrees of change and uncertainty, increasingly posed by AI in some sectors. A critical educational challenge involves figuring out how to support young generations to develop the capabilities that they will need to adapt to, and innovate in, a world with AI. This article argues that both educators and learners should be involved not only in learning but also in co-designing for learning in an AI world. Further, they together should explore the knowledge, goals and actions that could help people shape future AI scenarios, and learn to deal with high degrees of uncertainty. A key contribution of the paper is a re-conceptualization of design for learning in an AI world, which explores a problem space of educational design, and illustrates how educators and learners can work together to re-imagine education futures in an AI world. As part of this problem space, the paper discusses underpinning philosophies (the capability approach and value creation), a high-level pedagogy (with an emphasis on co-creation), pedagogical strategies (speculative pedagogies), and pedagogical tactics (AI scenarios). It then proposes a design framework (ACAD) to support educators and learners' discussions about design for learning in an AI world. This participatory design approach aims to sensitize people for what education may mean, for whom, and how learning with AI may look like, and it highlights the active engagement of educators and learners in co-designing a future they desire, to help shape learning and living in an AI world.