Résumé: It goes without saying that the AIED community represents a distinctive and central role in the current research and debate around the use of AI in educational settings. Like any other community or field of study, AIED represents a particular set of intellectual and methodological frames or 'knowledge tradition' (Furlong & Whitty, 2017). Although it is not possible in such a short essay to go into any ontological and epistemological discussions of this knowledge tradition in depth, work in the AIED community tends to be broadly characterised by a desire to use technology as the primary way to address perceived problems in education. This view of technology and learning can be seen as an "engineering view of education" (Oliver, 2016; drawing on Peters, 2006) where technology (in this case AI systems) are seen as relatively neutral tools that can be developed to achieve specific learning outcomes (Williamson & Eynon, 2020). The precise theory of learning differs (and has largely moved from cognitivist to more constructivist modes) as has the range of AI techniques and models to achieve learning goals (facilitated by significant advances in machine learning). Yet, the underlying belief that technology can support learning in making it more efficient and effective if it is designed appropriately remains. The most typical research methods employed are randomised control trials and other experimental designs to test causal models (Oliver, 2016). Such approaches align with a knowledge tradition that can be described as the "New Science of Education", where Education can primarily be studied through using methodologies from the natural sciences, and is very focused on questions of learning outcomes and other statistical measures of student achievement. It is an area of revival, with many academic supporters, and one heavily supported by education policy actors both nationally and internationally (Furlong & Whitty, 2017).

Langue: Anglais