The Credential Mirage in AI-Era Higher Education: A Counterfactual Competence Audit for Trustworthy Degrees
Keywords:
higher education, generative artificial intelligence, assessment validity, graduate competence, learning transfer, counterfactual competence auditAbstract
Generative artificial intelligence (AI) can improve the quality and speed of student outputs while weakening the inference that a university grade represents independently transferable graduate competence. This article names that potential divergence the credential mirage: a failure of credential interpretation, not proof that AI necessarily reduces learning. It asks how higher education can preserve valid degree claims without excluding legitimate AI augmentation. An analytical design-science approach integrates argument-based assessment validity, cognitive offloading, retrieval and transfer research, evaluative judgment, human-AI collaboration, and responsible educational technology governance. The article develops the Counterfactual Competence Audit (CCA), a program-level assurance artifact that triangulates three complementary forms of evidence: bounded independent performance, accountable AI-augmented performance, and delayed transfer to a meaningfully changed task. Six design principles, an operational rubric, a decision protocol, and five falsifiable propositions are specified. Ex ante analyses of three illustrative cases identify where conventional assessment might overclaim competence, where AI legitimately expands performance, and where inequitable testing conditions could create false failure signals. The contribution is a validity-centered design for degree assurance that distinguishes the quality of an AI-assisted artifact from evidence of what the graduate can explain, adapt, and responsibly delegate. No new learner experiment is claimed. The paper concludes with a feasible pilot design and boundary conditions for universities, accrediting bodies, educators, and employers.
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