Evaluative conversations as an AI-resilient, effective and inclusive form of assessment

Ivimey-Cook, Edward R. ORCID: https://orcid.org/0000-0003-4910-0443, Nash, Will ORCID: https://orcid.org/0000-0002-6790-1167, de Coriolis, Jean-Charles, Leggett, Helen, Wagstaff, Laura and Edmunds, Kelly ORCID: https://orcid.org/0000-0001-5351-3721 (2026) Evaluative conversations as an AI-resilient, effective and inclusive form of assessment.

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Abstract

The rise in artificial intelligence has accelerated the need for Higher Education Institutions worldwide to rethink their approach to student assessment. One candidate receiving increased attention in tackling widespread AI usage and to promote academic integrity is the use of evaluative conversations as a form of interactive oral assessment. However, despite qualitative studies suggesting beneficial evidence, to date quantitative analysis of this form of assessment has been lacking. Here, using data from a first-year cohort of undergraduate students we critically assess the effectiveness of evaluative conversations as a form of assessment overall and across two different demographic groups; those with/without declared disabilities and age at start of course (mature vs non-mature). We find evidence that evaluative conversations are an effective and inclusive form of assessment, with students achieving marks similar to online course tests, regardless of demographic group. With an appropriate design in place, evaluative conversations can be a powerful addition to the assessment toolkit of Higher Education institutions.

Item Type: Article
Faculty \ School: Faculty of Science
Faculty of Science > School of Biological Sciences
UEA Research Groups: Faculty of Science > Research Groups > Biosciences Teaching and Education Research
Faculty of Social Sciences > Research Centres > Centre for Higher Education Research Practice Policy and Scholarship
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 08 Sep 2026 11:13
Last Modified: 13 Sep 2026 08:52
URI: https://ueaeprints.uea.ac.uk/id/eprint/104460
DOI: 10.35542/osf.io/5fyq4_v2

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