The End of Assessment? Disruption and Transformation in the Age of AI

John D. Hathcoat, Ruth Slotnick, Will Miller   |    Volume 21 Issue 3  |   

Generative artificial intelligence is accelerating a profound disruption in educational assessment by collapsing long-standing boundaries between learner, test, and assessor. Large language models can generate human-like performances, design items and rubrics, score complex work, and synthesize institutional findings, thereby resulting in an epistemic shift in what counts as evidence and learning. We argue that the traditional assessment paradigm—grounded in fixed data points, disciplinary expertise, and representational measurement—is dissolving. In its place is emerging a new ecology of evidence in which AI and humans co-produce knowledge, thereby requiring assessment professionals to evolve into a “cyborg” practitioner who is fluent in both educational assessment and AI literacy. We outline how assessment must transform from a system of static measurement procedures to one centered on meaning-making, justice, and ethical stewardship of AI-augmented evidence. Using the national Community of Practice as a case in transition, we offer a developmental pathway for rebuilding assessment in a world of human-AI partnerships.

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