oral-defense
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- PaperEdArXiv (OSF Preprints)20 Jul 2026
The Forcing Function: Ownership, Provenance, and Oral Defense in AI-Assisted Student Work
Greg O'Keefe
This paper presents an assessment redesign using oral defense to ensure student ownership in AI-assisted work. The framework assigns AI a narrow role (locating material) while requiring students to verify sources and construct an independent position, tested via a two-probe oral defense. The approach is designed for secondary classrooms with small class sizes and instructor familiarity.
Original abstract
Institutional responses to generative AI have largely relied on detection. Detection has failed, and Papers One and Two of this series argue that the failure is structural rather than technical: a finished piece of writing does not carry reliable evidence of the process that produced it, and no improvement in detection tools alters this. This paper proposes a different response. Rather than attempting to establish after the fact whether a student used AI, it redesigns the assessment so that success depends on a capacity AI cannot supply on the student's behalf. The instrument is oral defense, though not as a means of detecting deception. Oral defense is analyzed here as a forcing function: it alters what a student must do in advance in order to succeed, and therefore shapes behaviour before the assessment rather than adjudicating it afterward. The paper develops three connected components. The first is a research protocol that assigns AI a narrow role, the location of material, and reserves for the student the two operations that constitute ownership: verifying what each source argues, and constructing a position from the verified set. The second is a rule of exclusion: a claim whose primary source the student cannot obtain and read may not function as a premise in the argument, even in hedged form. The third is a two-probe defense that tests these two operations separately, incorporating a live friction point that cannot be anticipated and scripted. Ownership, on this account, is not authorship of the prose. It is verified provenance together with demonstrated independent synthesis. A student who used AI extensively and satisfies both conditions passes; a student who used no AI and satisfies neither fails. This is not a loophole in the framework but its central commitment. The paper is addressed to the secondary classroom rather than the university lecture hall, and the scope is integral to the argument rather than incidental. The mechanism developed here depends on an instructor who knows their students across a term, on class sections of roughly twenty to thirty, and on a developmental stage at which the knowledge being assessed is still forming rather than already established and merely being applied. Existing AI-resilient assessment frameworks, examined in Section 5, are designed for university cohorts in the hundreds, where that knowledge is assumed largely in place. The two scopes are not competing solutions to a single problem; they are solutions to different problems, and this paper claims only its own.