{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T07:25:58Z","timestamp":1793604358961,"version":"4.5.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Generative AI tools can produce polished academic text on demand, undermining the validity of assessments that treat written submissions as evidence of individual learning. Detection-based countermeasures have demonstrated variable accuracy and equity concerns. This paper does not report empirical outcomes or validation data. It proposes a framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning. The framework comprises four pillars: (1) process-based documentation; (2) oral defense integration; (3) authentic task design; and (4) transparent AI-use policies aligned with intended learning outcomes. These are operationalized through a rubric model, an oral-defense protocol, and an assessment vulnerability audit tool. Illustrated primarily through health sciences education, with wider relevance to professional and practice-oriented disciplines, the framework treats generative AI as a catalyst for re-examining how assessment evidence is generated rather than solely as an integrity threat. All proposed tools are design instruments for future empirical evaluation, not psychometrically validated instruments.<\/jats:p>","DOI":"10.3389\/frai.2026.1841682","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:36:19Z","timestamp":1784180179000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Designing AI-resilient assessment in higher education: a four-pillar conceptual framework"],"prefix":"10.3389","volume":"9","author":[{"given":"Dragan","family":"Nikoli\u0107","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University of Novi Sad","place":["Novi Sad, Serbia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marijana","family":"Basta Nikoli\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Novi Sad","place":["Novi Sad, Serbia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1499495","DOI":"10.3389\/feduc.2024.1499495","article-title":"AI-resistant assessments in higher education: practical insights from faculty training workshops","volume":"9","author":"Awadallah Alkouk","year":"2024","journal-title":"Front. 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