Rethinking assessment in the AI age
Type: other
Abstract: Generative artificial intelligence is challenging long-held assumptions about assessment in higher education. Rather than framing AI solely as a threat to academic integrity, this article argues that its emergence presents an opportunity to reconsider what assessment is intended to measure and how evidence of learning is demonstrated. It contends that traditional approaches, particularly those reliant on unsupervised written coursework, are becoming increasingly inadequate as reliable indicators of individual capability.Drawing on contemporary developments in digital pedagogy and assessment practice, the article proposes a shift from designing assessments that resist AI towards assessments that foreground higher-order capabilities, including critical thinking, judgement, creativity, reflection and the authentic application of knowledge. It argues that assessment should prioritise evidence of learning processes and transferable capability over the production of artefacts alone.The article contributes to the ongoing sector-wide discussion surrounding assessment redesign by positioning AI not as the source of the assessment challenge, but as a catalyst exposing limitations that already existed within established assessment models. It calls for a strategic and evidence-informed approach to assessment that prepares graduates for an AI-enabled world while maintaining academic standards and supporting meaningful student learning.
Author: Johnson, Clay
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7204333467