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IntegrityShield

This moonshot tackles a growing grand challenge: restoring trustworthy academic and workforce credentials in an era where generative AI enables students to complete evalua- tion tasks without mastering skills. As AI reshapes hiring and reskilling, the credibility of certificates and degrees, the primary sig- nals the labor market relies on to identify ability, is at risk, especially for community college students, online learners, and working adults who depend on flexible assessments for mobility. IntegrityShield addresses this crisis by pairing invisible, document-layer preven- tion with verifiable, low–false-positive detection that integrates directly into LMS systems & hiring platforms without requiring redesigning assignments or monitoring students. This lightweight approach strengthens typical assessments by disrupting chatbot reuse and embedding visible-to-humans but machine-detectable integrity markers. Our goal is a scalable, non-surveillance solu- tion that preserves assessment credibility, protects honest learners, and restores the value of credentials for hiring and reskilling. A six-month proof-of-concept will deliver a pilot-ready prototype, open verification libraries, and deployment across ∼2,400 students, laying the foundation for a prevention-first assessment engine that rewards reasoning skills modern AI cannot reliably reproduce.