{"resourceId":"johnson-equity-first-genai-he-playbook-20260907","versions":[{"version":"external-0558b6cecb290840924050e5d151e3a7bc1829ab9d7870f196fe56012f155397","resource":{"id":"johnson-equity-first-genai-he-playbook-20260907","title":"Educational Policy playbook: equity-first, NIST-aligned GenAI governance for U.S. higher education","organization":"Augusta University (authors); Educational Policy / SAGE","sector":"Higher education / policy research","geography":"United States","publishedAt":"2026-09-07","publicationDate":"2026-09-07","eventDate":null,"sourceName":"Educational Policy (SAGE)","sourceLabel":"Peer-reviewed OnlineFirst article (abstract + author Kudos summary opened; full PDF paywalled)","sourceUrl":"https://journals.sagepub.com/doi/10.1177/08959048261481544","evidenceClass":"standards-guidance","outcomeClass":"emerging","topics":["governance-procurement","data-security","accessibility-workforce","operating-model"],"finding":"OnlineFirst (7 Sep 2026) policy-design analysis proposes an equity-first, risk-calibrated GenAI playbook for U.S. HE integrating equity-centered leadership with NIST AI RMF: risk-tiered use-case portfolios, clear decision rights, procurement/vendor assurance, and impact assessments anchored in privacy, accessibility, nondiscrimination, and due process; flags shadow AI and decision-support tools becoming determinative. Why now: still absent from student-success archive after 2026-09-18 SHAPE warnings and ACC soft-launch of AI student-support tooling—fills operating governance gap for student-facing AI.","sledRelevance":"Gives U.S. SLED campuses a concrete control set for student-success GenAI (early alert, tutoring, advising bots) beyond syllabus statements—tie procurement and due process to equity outcomes.","evidence":"Policy-design synthesis of federal guidance, institutional policy patterns, and illustrative state/system levers; not an empirical outcome study. Full PDF not opened this run.","architectureImplications":"Map student-success AI use cases into risk tiers with documented decision rights and audit trails before wiring SIS/LMS data into agents.","governanceImplications":"Stand up proportionate controls—impact assessments, vendor assurance, participatory feedback—without waiting for a parallel bureaucracy.","securityPrivacyImplications":"Treat student-support GenAI under privacy, accessibility, and nondiscrimination duties; vendor assurance must address education-record handling.","caveats":"Playbook/guidance, not measured impact; claims limited to abstract/Kudos summary because full PDF was paywalled.","streamIds":["student-success"],"roles":{"sales":"For presidents and student-success leaders racing to deploy GenAI advising/tutoring, this Educational Policy playbook argues adoption is outrunning governance and offers NIST-aligned, equity-first controls—risk tiers, decision rights, vendor assurance, impact assessments. Use it to structure discovery on privacy, accessibility, nondiscrimination, and due process before promising transformation. Bounded value is a governance readiness engagement, not a guaranteed risk reduction from buying a platform.","engineering":"Engineering takeaway: classify student-success AI use cases by risk, encode decision rights, and require vendor assurance plus impact-assessment artefacts in architecture review. Prerequisites: data classification for SIS/LMS feeds and counsel-approved due-process paths for AI-influenced student decisions. Constraints: guidance paper without empirical impact data; full text paywalled here. Security: FERPA-aligned processing and audit logs. Proof of value: completed use-case inventory and signed assurance packages for top-tier tools.","delivery":"Delivery work: inventory shadow and official GenAI student-support tools, assign risk tiers, draft impact assessments, and stand a lightweight review committee. Ownership: provost-designated AI governance lead with student-success and IT. Skills: policy writing and vendor diligence. Governance checkpoints: public control summary and appeal paths for AI-influenced actions. Acceptance: tiered portfolio published and top tools assured; risks include paperwork without enforcement and ignoring shadow AI."},"roleDetails":{"sales":{"customerProblem":"GenAI student-support tools are launching faster than campuses can audit equity, privacy, and due-process risks.","stakeholders":["provost / president","student success leadership","general counsel / compliance","IT / security","student government"],"discoveryQuestions":["Which student-facing AI use cases are risk-tiered today?","Where do decision rights sit when AI flags a student?","What vendor assurance and impact assessments exist?"],"valueHypothesis":"Bounded advisory value: accelerate a NIST/equity-aligned control baseline before scaling early-alert or tutoring AI.","potentialEngagement":"4-week governance readiness assessment producing a risk-tiered portfolio and gap list—not a product implementation.","unsupportedClaims":["Playbook guarantees legal compliance","Controls alone eliminate disparate impacts","Empirical proof of reduced student harm from this paper"]},"engineering":{"fit":"Governance and assurance layer over existing SIS/LMS/analytics and GenAI student-support tools.","architectureIntegration":"Use-case register → risk tier → approval workflow → vendor DPA/assurance → runtime logging for high-tier tools.","prerequisites":["Authoritative inventory of student AI tools","Data classification scheme","Counsel templates for impact assessments"],"constraints":["Guidance not empirical evaluation","Full article body not retrieved","State law variation"],"security":"High-tier student-support AI must meet education-record controls, access logging, and retention limits.","proofOfValue":["100% of high-tier tools have assurance docs","Decision-rights matrix published","Shadow-AI discovery completed"]},"delivery":{"work":"Run inventory, tiering workshop, impact-assessment pilots on two student-success tools, publish control summary.","dependencies":["Counsel","IT security","student-success ops","procurement"],"ownership":"Provost-designated AI governance lead.","skillsAdoption":"Train managers that decision-support outputs need human review before consequential student actions.","governanceCheckpoints":["Committee charter","First impact assessments approved","Public summary posted"],"acceptanceCriteria":["Tiered portfolio live","Vendor assurance for top tools","Appeal path documented for AI-influenced decisions"],"risks":["Paper compliance","Shadow AI persists","Over-bureaucracy slows helpful tutoring tools"]}},"retrievedAt":"2026-09-22T03:13:20Z","enrichedAt":"2026-09-22T03:16:00Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Equity-first framing requires accessibility and nondiscrimination checks on student-facing GenAI—not optional afterthoughts after launch.","procurementImplications":"Build vendor-assurance and impact-assessment requirements into RFPs for advising/tutoring/early-alert AI.","operatingModelImplications":"Create standing decision rights and review cadence for student-success AI rather than one-off policy memos.","sourceVerification":{"responseId":"curl-sage-epx-08959048261481544","openedUrl":"https://journals.sagepub.com/doi/10.1177/08959048261481544","referenceExcerpt":"adoption of generative artificial intelligence (GenAI) is expanding faster than many campus governance systems can guide, monitor, or audit","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}