Publications and Research
Document Type
Article
Publication Date
Summer 7-3-2026
Abstract
The emergence of large language models has precipitated a fun damental disruption to higher education assessment. Conventional instruments are susceptible to AI-assisted completion, threatening construct validity across disciplines and institutional contexts. When submitted work reflects AI capability rather than student competency, the inferential chain from performance to qualifica tion is invalidated – a problem of design rather than detection. This systematic review makes two contributions. First, it theorises and grounds the distinction between AI-resistant assessment, which seeks to prevent or impede AI access through containment, and AI-resilient assessment, which assesses genuine human cogni tive performance of intrinsic educational value regardless of the AI tools that exist. Second, it synthesises peer-reviewed empirical lit erature, policy documentation, and sector guidance published between 2022 and 2025. A PRISMA 2020-compliant search of five databases was conducted; two independent reviewers screened all records with substantial inter-rater agreement. Forty-seven peer-reviewed studies, 23 policy documents, and 11 sector reports were included. Six evidence-based assessment categories were identified, taxonomised, and evaluated against a structured adversarial threat taxonomy. AI-detection tools are structurally insufficient as primary responses. A Six-Layer AI-Resilient Assessment Stack, grounded in multi-trait multi-method validation logic, is proposed as an integrated institutional framework. Persistent challenges include equity, reliability, faculty development, and data protection.
Included in
Curriculum and Instruction Commons, Educational Assessment, Evaluation, and Research Commons
Comments
This article was originally published in Assessment & Evaluation in Higher Education, available at htttps://doi.org/10.1080/02602938.2026.269537