TEACHING BY THE ALGORITHM: PROMISE AND PERIL IN INDIVIDUALIZED LEARNING Exploring Student and Teacher Perspectives on Individualized Learning

Abstract

Artificial Intelligence in Education (AIEd) has enabled new forms of learning through individualized learning systems. Systems that promise to personalize instruction and reduce teacher workload. These systems offer real-time feedback, personalized assignments, and learning analytics that support both students and teachers. While there are potential benefits, there are also potential negative consequences from their implementation. This thesis investigates how teachers and students view the potential implications of implementing individualized learning. A workshop using a provotype, an exaggerated, deliberately provocative prototype, was used to elicit concerns, reactions, and reflections that might have otherwise remained hidden. A thematic analysis following Braun and Clarke’s six-phase method found four themes. First, participants valued the potential to automate administrative tasks and progress tracking, allowing teachers to save time and provide timely, targeted support to students. Second, individualized learning was perceived to increase student engagement and motivation. However, participants also expressed concerns about the risk of fragmenting classroom dynamics. Individualized tasks may isolate students and undermine collaborative learning. Finally, ethical concerns were raised regarding algorithmic influence, including overreliance, risks of reinforcing inequalities, while highlighting the importance of keeping human autonomy. These findings highlight a tension between the technical promises of individualized learning and its social and pedagogical implications. While there is potential for improving education, current empirical evidence remains limited. Implementation must therefore be approached with caution. Individualized learning must not simply be seen for its possibilities for efficiency, but as a system that reshapes classroom dynamics, autonomy, and fairness, prioritizing pedagogical value over technological capabilities