Policy makers are facing a teacher recruitment and retention crisis. The numbers of people training to be teachers is steadily falling, and once trained, a significant proportion of teachers are not staying in the profession for a long time- period. Two central reasons for this are teacher workload and the nature of teachers work. Policy makers are increasingly looking to AI as a solution to these challenges, exploring ways it could potentially be used to save teacher time and free up the teacher from mundane tasks.
However, these promises of automation and the freeing up time of teachers have been around for decades. Beyond the claims from EdTech vendors, there is very little evidence of the ways in which AI is, or could, save teachers time or how AI may be changing the nature of teachers’ work, and indeed potentially make it less meaningful. Of what evidence does exist, the methods commonly used do not capture the complexity of what actually happens in classrooms and schools.
This study seeks to explore how AI is reconfiguring teachers’ work by addressing the following questions:
Professor of Education, the Internet and Society
Project role: Principal Investigator
Rebecca Eynon's research focuses on learning and the Internet, and the links between digital and social exclusion.
Associate Professor of Digital Education, Department of Education, University of Oxford
Project role: Co-Investigator
Jeremy Knox is Associate Professor of Digital Education at the Department of Education, University of Oxford, and an Official Fellow of Kellogg College.
Postdoctoral Researcher
Project role: Researcher
Laura is a postdoctoral researcher on the ESRC-funded project “Towards equity focused approaches to EdTEch: a socio-technical perspective.” Her research interests lie at the intersection of learning, technology, ethics and inequality.
