🏷️ Concept
Academic Integrity
Academic integrity — the ethical framework governing honest academic work in the age of AI. The wiki documents how the concept has been reframed by generative AI: from a problem of detecting dishonest output to a design problem of making honest work visible, verifiable, and worth producing. Academic integrity research in this space has evolved from detection-focused approaches toward fundamental assessment redesign, pedagogy-led governance, and teaching students how to use AI well rather than merely policing whether they do.
The arrival of generative AI has not created the need for academic integrity — it has made weaknesses in existing approaches harder to ignore. A polished, plausible product can now be generated in seconds, so product resemblance is an increasingly unreliable signal of capability. This shifts the integrity question from "can we catch AI use?" to "can our assessments still warrant the inferences we draw about student learning?"
The evolution from detection to redesign
The rationalization problem
Students do not generally misuse AI out of malice; they rationalize it. Interview research identifies at least five disconnect sites where students' interpretation of AI policy diverges from faculty intent, and a taxonomy of 20+ distinct rationalizations — from "copying AI text is victimless" to "text reflecting my beliefs is my own writing." These rationalizations are ad hoc, post hoc, and internally inconsistent, and they describe a "steep, ethical slippery slope" on which students slide far outside pedagogical goals. This is why student misconceptions about AI are the upstream cause of integrity violations, and why integrity education must address ethical reasoning, not just technical skill.
Why policy alone fails: the coordination problem
A coordination-game framework provides a mechanism-level account of why policy pronouncements rarely change behavior: students' AI use is a coordination problem, where individual choices depend on peer expectations and assessment design. The model's key finding is non-linear threshold dynamics — small, well-calibrated changes to reflective-assessment incentives can trigger rapid cohort-wide shifts toward responsible use, while weak or misaligned incentives let opportunistic practice persist. In practical terms, modest redesign (e.g., requiring students to reflect on their AI interactions) can have disproportionate effects where abstract rules have none.
The socio-emotional dimension
Integrity enforcement has a neglected emotional cost. Shame-and-guilt research with students shows these emotions regulate when and how AI use becomes visible, producing hiding behaviors and selective disclosure — and that they coexist with continued use, creating cycles of reduced agency and moral tension rather than behavior change. Students even describe their AI use in language of addiction. The implication: detection-heavy, surveillance-oriented policy risks driving misuse underground rather than addressing it, undermining the candid negotiation that productive use requires.
Cultural and contextual variation
Policy text does not equal policy perception. Cross-national research found that, despite functionally identical institutional policies, students at different universities rated the same AI-assisted practices differently — culture, not policy wording, drove perceived wrongness. Policy harmonization does not produce perception harmonization, so culturally diverse cohorts interpret the same rules differently, an equity concern for enforcement and grading that argues for scenario-based clarification over abstract rule statements.
From policing to pedagogy
The wiki documents a paradigm shift: from AI as an integrity threat to be policed, to AI as a tool whose appropriate use must be taught. This is the ethical dimension of AI Literacy and is embodied in practical design:
Connections
Academic integrity connects to Assessment Validity, AI Literacy, Plagiarism Detection, Authentic Assessment, Assessment, Educational Policy AI, Regulation, Ethics, and Equity. It is the ethical dimension of AI in education, inseparable from Over Reliance and the broader question of how Generative AI reshapes Higher Ed and K 12 learning.