Detection-led responses face well-documented limits: validity and fairness failures (bias against non-native writers), notable error rates, erosion of trust, and distraction from assessment design. Detection should be a limited, situational tool — not a strategy of first resort. The constructive question is not "how do we prevent students from using AI?" but "how do we enable them to use it th

Beyond Detection: authentic assessment in an AI-mediated world

Kickbusch, Ashford-Rowe, Kemp, Boreland & Huijser (2025) argue the dominant institutional response to generative AI in assessment — surveillance and AI detection — misdiagnoses the problem: in an AI-mediated world, authenticity cannot be policed into existence; it must be redesigned. They reconceptualise authenticity as constructed where AI is expected, declared, and scrutinised, and offer discipline-agnostic "design for learning" patterns that position AI as a collaborator rather than a cheating application.

The case against detection

Detection-led responses face well-documented limits: validity and fairness failures (bias against non-native writers), notable error rates, erosion of trust, and distraction from assessment design. Detection should be a limited, situational tool — not a strategy of first resort. The constructive question is not "how do we prevent students from using AI?" but "how do we enable them to use it thoughtfully, responsibly, and effectively in contexts that mirror their future work?" Excluding AI from assessment creates an inauthentic scenario: the authentic professional justifies when, how, and why they use tools, and critically evaluates their outputs.

Authenticity as a four-dimensional continuum

Not a binary but a continuum across four intersecting dimensions:

1. Task–context alignment with contemporary professional practice — judgement, decision-making, and problem-solving under uncertainty, not superficial workplace replication

2. Foregrounding professional judgement and ethics — sustainable assessment (Boud & Soler 2016), collaboration (Boud & Bearman 2024), UNESCO 2023 capability framing

3. Visibility of process — iteration, critique, rationale; polished outputs can mask superficial understanding, so assessment must reveal the "messiness" of authentic professional work

4. Appropriate use of tools (including AI) within human decision-making — tools as enablers of higher-order capability, not substitutes for it

Stage-appropriate authenticity: early units get constrained, well-scaffolded tasks; later units open complexity, uncertainty, and stakeholder engagement.

Design patterns ("design for learning moves")

Challenges and institutional responsibilities

Connected Concepts

Connected Articles

Citation

Kickbusch, S., Ashford-Rowe, K., Kemp, A., Boreland, J., & Huijser, H. (2025). Beyond Detection: Redesigning Authentic Assessment in an AI-Mediated World. Education Sciences, 15(11), 1537. DOI