Applied Silences in Artificial Intelligence in Education: A Five-Dimensional Framework and the Case for Audible AIEd

Artificial intelligence (AI) has moved rapidly from the periphery of educational technology to the centre of pedagogical, administrative, and assessment practice. The dominant discourse surrounding AI in education (AIEd) emphasises personalisation, efficiency, scalability, and inclusion. This manuscript argues that alongside these celebrated affordances, AIEd produces a less visible but consequential phenomenon: applied silences. Applied silences are the systematic quieting of voices, knowledge forms, pedagogical processes, and critical questions that are either deliberately designed away or structurally rendered inaudible by AI systems in education. Drawing on a synthesis of recent scholarship across IEEE learning technologies, critical pedagogy, ethics of automated assessment, and surveillance studies, the manuscript develops a five-dimensional framework of applied silence in AIEd: algorithmic epistemic silence, pedagogical silence, dataveillant silence, structural and policy silence, and cognitive and affective silence. For each dimension, the mechanisms, evidence, and educational consequences are examined. The manuscript then proposes a counter-framework of audible AIEd, centred on preserving productive uncertainty, redistributing epistemic authority, enforcing genuine human oversight, and designing governance for voice rather than compliance. The conclusion argues that the legitimacy of AI in education will depend not only on what these systems enable, but on what they allow to remain heard.