With a rapidly emerging and unexplored, digital educational landscape as backdrop, my research aims to explore how Primary Education teachers' teaching practices are affected and (reshaped) by educational technologies with artificial intelligence (AI) that apply machine learning (ML).

ML can be described as the scientific study of how computer systems can “learn” from data without being programmed in specific ways. In a teaching context, ML-based educational technologies can be used, for example, to automate certain tasks such as planning, personalization and assessment. These technologies are beginning to enter classrooms around the world, strongly supported by recent years policy reports and the interdisciplinary research field Artificial Intelligence in Education (AIED) and accelerated by the COVID 19 pandemic. Meanwhile practice-based educational research in the field is scarce. I want to understand how teachers' practices are affected when ML-based technologies are introduced in the classroom and conversely how ML-based teaching materials are affected by teachers. Through an exploratory approach grounded in Actor- Network-Theory, I also wish to make visible the ethical implications for teachers and students that follow with the use of ML-technologies in education.

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Katarina Sperling, Cormac McGrath, Linnéa Stenliden, Anna Åkerfeldt, Fredrik Heintz (2023) Mapping AI Literacy in Teacher Education 2nd International Symposium on Digital Transformation: August 21-23, 2023, Linnaeus University, Växjö