Purpose Before Policy: Academic Integrity, Generative AI, and Rhetorical Stance

Synthesis: Taylor and LaCroix argue that whether generative AI use constitutes misconduct depends on a prior question: what is the university's purpose? Drawing on an historical and rhetorical account of the Anglo-American university, they analyze mission statements from leading university networks to show that rising GenAI-related misconduct reflects structural incoherence in the neo-liberal university, where technological enthusiasm, corporate influence, and policy enforcement often conflict. They conclude that universities cannot credibly enforce integrity standards in the age of AI without first ensuring coherence between their stated missions, pedagogical practices, and approaches to emerging technologies.

Key Findings

1. Purpose precedes policy. Whether GenAI use is misconduct cannot be settled in isolation; it depends on an institution's purpose, ranging from knowledge creation and transfer to community/values and societal engagement.
2. Structural incoherence drives misconduct. Apparent rises in GenAI misconduct reflect misalignment among stated ideals, incentive structures, assessment design, and administrative practice—treating it as individual behavior misdiagnoses the problem.
3. Mission statements are strikingly uniform. A qualitative rhetorical analysis of the Canadian U15, UK Russell Group, and US Ivy League found a shared vocabulary of "research," "students," "teaching," "community," "knowledge," "learning," and "commitment," organized around three functions: knowledge creation/transfer, community and values, and societal/global engagement.
4. Ethos overwhelms logos and pathos. Mission statements evidence Booth's "Entertainer's" and "Advertiser's" stances—institutions define value by their own existence or appeal to multiple audiences at once—obscuring structural constraints such as financial precarity and inequitable access.
5. Use is widespread while policy lags. A 2024 Global AI Student Survey found 86% of students use AI tools while 80% report institutional policies are unclear or inadequate.
6. Universities simultaneously endorse and police AI. Blanket endorsements of AI's potential coexist with bans and punishment, producing contradictory policy signals and systemic incoherence that erodes institutional credibility.
7. Governance is shaped by private partners. Policies are increasingly developed with corporate and for-profit actors (e.g., the Digital Education Council), blurring the university's public mission and creating opacity in AI Governance.
8. Academic integrity is a collective practice. It is an inherently collective, epistemic undertaking that cannot be credibly demanded while institutions fail to communicate clearly, align practice with mission, or preserve the epistemic norms that underwrite knowledge transfer.

What this means for practice

• Administrators. Resolve the misalignment among mission, pedagogy, and technology stance before tightening integrity enforcement, since the authors argue misconduct cannot be credibly adjudicated while an institution simultaneously endorses and polices AI.
• Administrators. Replace ethos- and pathos-driven mission language with procedurally clear guidance — who decides, on what grounds, with what route of appeal — because uniform mission vocabulary across the U15, Russell Group, and Ivy League obscured structural constraints such as financial precarity and inequitable access.
• Administrators. Reduce corporate influence over policy formation, since governance is increasingly developed with private and for-profit actors such as the Digital Education Council.
• Instructors. Treat academic integrity as a collective epistemic practice and align course expectations with the stated institutional purpose, rather than enforcing it as an individual obligation.
• Researchers. Scrutinize mission statements as active rhetorical interventions — sites where institutional identity is constituted and where the gap between aspiration and practice becomes visible.

Limitations

• The mission-statement analysis is illustrative rather than comprehensive: it covers three Anglo-American networks (the Canadian U15, the UK Russell Group, and the US Ivy League), and the authors state that a more comprehensive analysis across cultural contexts is needed.
• It is a rhetorical and historical argument, not a measurement study; the authors explicitly do not assume that mission statements provide operational guidance or directly determine institutional policy.
• The prevalence figures the paper relies on are secondary — a 2024 Global AI Student Survey reporting 86% student AI use and 80% describing institutional policies as unclear or inadequate — not data collected for this study.
• The claim that rising GenAI misconduct reflects structural incoherence is advanced by argument, without institutional or student-level data against which it could be tested.

Citation

Taylor, T. B., & LaCroix, T. (2026). Purpose before policy: Academic integrity, generative AI, and rhetorical stance. Higher Education.