Detection-oriented integrity approaches are increasingly misaligned with GenAI-augmented assessment (theoretical argument)¶

Claim · All claims
Evidence · 1 study · theoretical r3 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The paper argues that detection and verification-based approaches are increasingly untenable in GenAI-augmented assessment because generative text is probabilistic and shaped through iterative human prompting, making definitive attribution unreliable. → Sunaina Sharma 2026

Evidence¶

Sunaina Sharma 2026¶

Sunaina Sharma. (2026). Educational integrity in GenAI-augmented assessment: making judgement visible. International Journal for Educational Integrity 22:6. https://doi.org/10.1007/s40979-026-00216-y

q1 · i? · theoretical · r3

Theoretical argument in the section on why detection-oriented approaches are misaligned. The paper offers no empirical data for this claim; it reasons from the nature of generative text and cites integrity scholarship. It also notes detection tools may serve as one supplementary layer within multi-layer governance.

"Generative text is probabilistic, non-deterministic, and shaped through iterative human prompting and revision, rendering definitive attribution to either human or machine sources unreliable"

Discussion¶

Related Claims¶

• Judgement is theorized as the evaluative locus of educational integrity within GenAI-augmented authorship — related
• The article claims LLM-assisted workflows weaken the validity of output-based evaluation in hiring, education, and certification — related
• Assessment design functions as a pedagogical condition shaping how students engage with GenAI during learning — related
• Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment — related
• The paper argues authenticity in AI-mediated assessment is achieved through design rather than detection — related
• Perfect AI execution cannot remove four limits: accuracy does not settle end-setting, safety does not confer legitimacy, detection does not secure integrity, and outputs do not replace participation — related
• GenAI-era assessment reform should move beyond defensive approaches focused primarily on preventing misconduct or detecting AI use — possibly the same claim (merge candidate)