ABSTRACT

The use of data to govern education is increasingly supported by the use of knowledge-based technologies, including algorithms, artificial intelligence (AI), and tracking technologies [Fenwick, T., E. Mangez, and J. Ozga. 2014. Governing Knowledge: Comparison, Knowledge-Based Technologies and Expertise in the Regulation of Education. New York, NY: Routledge]. New forms of datafication and automation enable governments and other powerful stakeholders to draw from the past to construct images of educational futures in order to steer the present. This paper examines the competing conceptions of time and temporality that AI posits for policy and practice when used to anticipate educational futures. We argue that most educational futures are already delineated, and machinic expressions of time are the chronologies, habits, and memories that the educated subject inhabits rather than produces. If resetting educational habits and memories can be an alternative to algorithmic anticipations of education then we believe, paradoxically, that machines may help to reset them by accelerating them.

摘要

基于数据的管理教育越来越受到基于知识的技术的支持，这些技术包括算法，人工智能（AI）和跟踪技术[Fenwick，T.，E. Mangez和J. Ozga。2014年。“管理知识：比较，基于知识的技术和教育法规方面的专业知识”。纽约，纽约：Routledge]。新型的数据化和自动化形式使政府和其他强大的利益相关者可以借鉴过去来构建教育前景的图像，从而引导当前。本文考察了AI在用于预测教育未来时为政策和实践所设定的时间和时间竞争概念。我们认为，大多数教育的未来已经被划定了，时间的机器表达是受过教育的主体所居住而不是产生的年代，习惯和记忆。如果重设教育习惯和记忆可以替代算法对教育的期望，那么我们反常地认为，机器可以通过加速机器来帮助重设它们。