Short abstract:

ML tools automate teaching tasks, challenging teachers' expertise, autonomy, and accountability. This paper explores these (contested) shifts in (professional) agency using ethnographic examples from Swiss secondary schools.

Long abstract:

As in other fields, machine learning (ML) tools are promising a technological solution to educational problems. Tasks that are considered central to teaching are increasingly automated by ML tools: Intelligent tutoring systems provide feedback and select appropriate exercises for individual students (task selection), automated grading evaluates essays and open-text exercises (assessment), and learning analytics promise to predict student performance based on their past actions tracked on learning platforms (diagnostics). This not only raises questions about the professional identity of teachers, but also disrupts the established distribution of agency between humans and machines in education. The proposed paper addresses this (contested) shift in the distribution of agency using empirical examples from an ethnographic project on the use of ML tools by teachers in Swiss secondary schools. Three dimensions of agency are identified that are particularly affected by ML tools: (1) expertise: in the face of (seemingly) objective tools, teachers are expected to justify their pedagogical decisions when they deviate from automated systems; (2) autonomy: consequently, teaching often involves defending the autonomy of the profession against the claims of companies promising better results; (3) accountability: Finally, the advent of ML tools in education also obscures questions of accountability by distributing it across a range of actors (teachers, administrators, developers, systems...) - who should one turn to when grades are perceived as unfair or learning outcomes as lacking? Conceptually, the paper draws on STS research on ML, mediated accountability, distributed agency, and the sociology of professions.