Trust, Tension, and Teacher Judgement: Rethinking Assessment Integrity in the Age of Generative AI

Trust, Tension, and Teacher Judgement: Rethinking Assessment Integrity in the Age of Generative AI

As generative AI tools rapidly enter classrooms and teacher workflows, questions about the nature of professional judgement, assessment integrity, and educator wellbeing are becoming increasingly urgent. This conceptual paper explores the epistemic and ethical tensions that arise when teachers are invited—or expected—to integrate AI into the assessment process. While AI promises efficiency, personalisation, and reduced administrative burden, its use in high-stakes educational contexts raises critical concerns: How is trust in teacher judgement reshaped when machines assist in designing or evaluating student work? What are the implications for the relational and interpretive dimensions of assessment practice? And, how do these shifts affect teacher wellbeing, particularly in relation to autonomy, workload, and professional identity? Drawing on emerging literature in AI ethics, assessment theory, and teacher professionalism, this paper positions the use of AI not merely as a technological enhancement, but as a profound reconfiguration of educational labour and epistemic authority. It argues that maintaining rigour, fairness, and human-centred decision-making in assessment requires a recalibration of how we understand both “authorship” and “expertise” in AI-mediated contexts. The presentation offers a provocation for rethinking teacher agency and accountability in this evolving space and invites dialogue about how we might uphold the integrity of educational assessment in an AI-augmented future.

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