Why this matters
Arms race dynamics in higher education reveal how well-intentioned institutional policies can transform educational relationships into adversarial ones. Understanding the structural conditions that create these dynamics shows why prohibition-focused responses to challenges like AI use tend to fail—and what might work instead.
Arms race dynamics in higher education
One-sentence definition: A pattern where institutions and students escalate defensive measures against each other in ways that displace educational goals, driven by misaligned incentives, trust deficits, and fixation on measurable proxies.
Harland and Wald (2021) applied the arms race metaphor to higher education assessment, describing how modular systems created competition between academics for student attention, mediated through grades. Students wouldn’t work without grades, academics wouldn’t set ungraded assignments, and cumulative averaging made every grade high-stakes. Educational benefit became secondary to grade accumulation. The same structural pattern now appears in institutional responses to AI tools—prohibition without purpose, detection-focused responses, punitive frameworks—creating adversarial dynamics where students develop increasingly sophisticated evasion methods.
Conditions that create educational arms races
Three structural conditions transform educational relationships into adversarial cycles:
Misaligned incentives - Parties optimise for different outcomes. In grade proliferation, academics wanted engagement while students wanted GPA protection; neither primarily optimised for learning. In AI prohibition, institutions want authenticity verification while students want efficiency and support; learning sits awkwardly between them.
Trust deficits - Institutional assumptions create self-fulfilling prophecies. The grade arms race assumed students wouldn’t work without grades. The AI arms race assumes students will cheat if able. Both assumptions shape policies that provoke exactly the behaviour they fear.
Measurement fixation - Complex educational interactions get reduced to quantifiable proxies. Grades proxy for engagement, detection software proxies for authentic work. The proxy becomes the target, displacing what it was meant to measure.
Limits of the metaphor
Arms races imply mutual escalation. Students aren’t escalating—they’re adapting to institutional escalation. The institution implements detection software; students find undetectable methods. The institution creates harsher penalties; students develop more sophisticated evasion. This is institutional power creating adaptive resistance, not mutual combat.
That distinction matters for intervention. If it’s truly mutual escalation, you need to de-escalate both sides. If it’s institutional behaviour creating resistance, the intervention point is institutional policy. The question isn’t “how do we stop the arms race?” but “why do institutions keep creating conditions that position students as adversaries?”
When it matters
Arms race dynamics are most visible in contexts where institutions attempt to control student behaviour through prohibition and surveillance rather than through aligned incentives and transparent rationale. High-stakes assessment environments, technology adoption periods, and academic integrity policies all create conditions where these dynamics can emerge. Recognition matters because it shifts focus from “how do we catch rule-breakers?” to “what about our system creates rule-breaking as a logical response?” These dynamics reflect deeper asymmetric power dynamics between institutions and students.
Sources
- Harland, T., & Wald, N. (2021). The assessment arms race and the evolution of a university’s assessment practices. Assessment & Evaluation in Higher Education, 46(1), 105–117. https://doi.org/10.1080/02602938.2020.1745753