From Forbid to Reimagine: Employer Strategies for Responding to Candidate GenAI Use in Assessment
Patrick D. Dunlop & Filip Lievens
Abstract
In this reply to the commentaries by Mirowska (2025), Hickman (2025), and Holtrop and Bronzwaer (2026), we expand on our initial provocation article regarding the effects of candidate use of Generative AI (GenAI) in personnel selection (Lievens and Dunlop, 2025). First, we update the discussion by highlighting recent technological developments (agentic AI and AI‐integrated wearables) that accelerate the threat of candidate GenAI use as a substitute for candidate effort. Second, we clarify and build upon our original arguments concerning the effects of candidate GenAI use on construct‐related validity and subgroup differences, introducing the concept of “GenAI literacy” as a potential confounding construct. In doing so, we elaborate on the concept of AI‐enabled assessment designs. Finally, we integrate the insights from the three commentaries with our own thinking to introduce the FAIR framework (Forbid, Advise, Insulate, Reimagine), which should help employers navigate the complex landscape of candidate GenAI use. The FAIR framework distinguishes between strategies aimed at preventing GenAI misuse and those designed to embrace and integrate GenAI into assessment processes. We conclude that the future of selection lies not in banning candidate GenAI use entirely. Instead, we argue for a strategic shift toward reimagining assessments for an AI‐augmented world.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.