Historical threads, missing links, and future directions in AI in education

Abstract

Artificial intelligence has become a routine presence in everyday life. Accessing information over the Web, consuming news and entertainment, the performance of financial markets, the ways surveillance systems identify individuals, how drivers and pedestrians navigate, and how citizens receive welfare payments are among myriad examples of how AI has penetrated into human lives, social institutions, cultural practices, and political and economic processes. The effects of the algorithmic techniques employed to enable AI are far-reaching and have inspired considerable epochal hype and hope, as well as dystopian dread, although they remain largely opaque and weakly understood outside of the social networks of technical experts (Rieder 2020). The profound social and ethical implications of AI, however, are becoming increasingly apparent and the objects of significant critical attention. AI is at the centre of controversies concerning, for example, automation in workplaces and public services; algorithmic forms of bias and discrimination; automated reproduction of inequalities and disadvantage; regimes of data-centred surveillance and algorithmic profiling; disregard of data protections and privacy; political and commercial micro targeting; and the power of technology corporations to control and shape all sectors and spaces they penetrate, from whole cities and citizen populations to specific collectives, individuals or even human bodies (Whittaker et al. 2018). Numerous ethical frameworks and professional codes of conduct have been developed to attempt to mitigate the potential dangers and risks of AI in society, though important debates persist about their concrete effects on companies or the way such frameworks and codes may serve to protect commercial interests (Greene, Hoffman, and Stark 2019). The current instantiation of AI on the Web, on smartphones, in social media, and in spaces via interconnected objects and sensor networks has a much longer history than some recent epochal claims would suggest. Histories of AI stretch back at least as far as the birth of computer science and cybernetics in the 1940s. The term 'artificial intelligence' itself was coined as part of a project and workshop at Dartmouth College in the mid-1950s. From the 1960s to the 90s, punctuated by periods of 'AI winter', AI research and development focused first on encoding principles of human reasoning to simulate human intelligence, and then on 'expert systems' that emulated the procedural decision-making processes of experts based on defined knowledge bases. After 2010, AI gradually returned under a new paradigm, not as simulated human intelligences or programmable expert systems but as data-processing systems that can learn and make predictions from classifying and correlating huge quantities of 'big data'. Computational processes including data analytics, machine learning, neural networks, deep learning and reinforcement learning underpin most contemporary forms of AI. AI is, perhaps, just a new catch-all name for a range of statistica...

Theories of Postdigital Heterogeneity: Implications for Research on Education and Datafication

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Exploring the impact of Artificial Intelligence and robots on higher education through literature-based design fictions

“…However, increasingly critiques of AI in learning centre around the datafication of education (Jarke and Breiter, 2019;Williamson and Eynon, 2020;Selwyn, 2019a;Kwet and Prinsloo, 2020). A data driven educational system has the potential to be used or experienced as a surveillance system.…”

“…It is evident that AI developments are often partly a marketing exercise (Lacity, 2017). Edtech companies play a dominant role in developing AI (Williamson and Eynon, 2020). Selwyn (2019a) worries that those running education will be seduced by glittering promises of techno-solutionism, when the technology does not really work.…”

“…Selwyn (2019a) worries that those running education will be seduced by glittering promises of techno-solutionism, when the technology does not really work. The UK government has invested heavily in gathering more data about HE in order to promote the reform of HE in the direction of marketisation and student choice (Williamson and Eynon, 2020). Learning data could also increasingly itself become a commodity, further reinforcing the commercialisation of HE.…”

Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education

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