AI and the reconfiguration of teachers’ work

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:

RQ1 – What aspects of teachers’ practice are most commonly subject to intervention by AI technologies?
RQ2 – To what extent are the logics of automation and augmentation apparent in these technologies?
RQ3 – What implications does this use of these AI technologies have on teacher workload, teacher identity, and pedagogy?