The clearest way to understand the framework is to watch it work. Each use case below is a common higher-education AI initiative, shown through the framework: the domain it sits in, the pillars it stresses, its risk tier, the real risks, and the governance path the framework prescribes.
Illustrative scenarios, not case studies of specific institutions. These are drawn from common patterns across higher education and are meant as a lens for applying the framework to your own situations. As institutions share documented, attributed examples, they'll join here, clearly marked as the real thing.
Teaching, Learning & Student SuccessIllustrative
Moderate
AI tutoring & writing assistant
A generative-AI assistant students use for explanations, practice, and writing feedback inside or alongside a course.
Value
Always-available, personalized support that can lift outcomes for students without access to tutoring, directly serving access and success missions.
Key risks
Over-reliance and erosion of skill-building
Academic-integrity ambiguity
Unequal access across the student body
Inaccurate or fabricated explanations
The framework's path
Moderate tier → streamlined review. Set course-level expectations (Pillar 3 syllabus guidance), build student literacy (Pillar 6), and pilot with success criteria before scaling (Pillar 8).
AI generates or drafts feedback and provisional scores on student work at scale.
Value
Faster, more consistent feedback and instructor time returned to higher-value teaching.
Key risks
Bias against non-native or non-standard writing
Opaque scoring students can't appeal
Academic-judgment displacement
FERPA exposure if work leaves approved systems
The framework's path
High tier → full review with safeguards. Require human-in-the-loop on any grade, a bias/equity check (Pillar 2/4), a named owner and appeal path (Pillar 7), and a risk-registry entry.
A model flags students as at-risk to trigger advising or intervention.
Value
Earlier, targeted support that can measurably improve retention and equity gaps, if done well.
Key risks
Bias that re-creates or widens equity gaps
Labeling effects and self-fulfilling prophecy
FERPA and consent
Acting on flags no human validated
The framework's path
Critical tier → highest scrutiny. Algorithmic impact assessment, fairness audit, cabinet + governance approval, continuous monitoring of override and equity rates, and a documented rollback plan. First ask: does a lower-risk alternative work?
Researchers use AI to search, summarize, and synthesize literature.
Value
Large time savings on discovery and synthesis; broader coverage of relevant work.
Key risks
Fabricated or misattributed citations
Subtle summarization errors
IP / licensing of source material
The framework's path
Low tier → light-touch. Acceptable-use guidance on verification and disclosure (Pillar 3) plus researcher literacy (Pillar 6). Self-certify and proceed.
A conversational agent answers routine IT, HR, or campus-service questions.
Value
24/7 first-line support, faster resolution, and staff time freed for complex cases.
Key risks
Confidently wrong answers on consequential topics
Undisclosed AI interaction
Scope creep into sensitive requests
The framework's path
Moderate tier → streamlined review. Disclosure that it's AI, clear escalation to humans, scoped topics, and monitoring of accuracy and handoff rates (Pillar 8).
AI scores, ranks, or filters applications to support admissions decisions.
Value
Throughput and consistency on high-volume review.
Key risks
Discrimination and disparate impact
Legal and regulatory exposure
Opacity in life-affecting decisions
Entrenching historical bias in training data
The framework's path
Critical tier → highest scrutiny, or reconsider. Mandatory algorithmic impact assessment, legal/compliance review, human decision authority, audit trail, and continuous disparate-impact monitoring. Often the right call is a narrower, lower-risk design.
Models optimize aid offers or enrollment targeting to hit institutional goals.
Value
Efficient use of limited aid and enrollment resources.
Key risks
Equity harm if optimization deprioritizes need
Misalignment with access mission
Opaque, consequential financial decisions
Compliance exposure
The framework's path
Critical tier. Test directly against the mission (Pillar 1), does optimization serve access or undercut it? Plus impact assessment, governance + cabinet approval, and ongoing equity monitoring.
A general assistant answers student questions on deadlines, services, and navigation.
Value
Lower friction for students, especially first-generation and working students navigating complex systems.
Key risks
Wrong answers on deadlines or requirements
Privacy of student queries
Accessibility gaps
Disclosure of AI use
The framework's path
Moderate tier → streamlined review. Accuracy guardrails on consequential info, accessibility compliance, clear AI disclosure, and pilot-then-scale with monitoring.
AI provides live captions, transcripts, or translation for events, media, and instruction.
Value
Broader access and inclusion; a direct equity win when done with human review.
Key risks
Accuracy failures on names, terms, or accents
Over-reliance without human correction
Privacy of recorded material
The framework's path
Low tier → light-touch. Adopt with a human-review expectation for high-stakes content and standard monitoring; a strong, mission-aligned early win to build literacy and trust.
AI scores alumni and prospects for giving propensity to target outreach.
Value
More efficient advancement and stronger donor relationships.
Key risks
Privacy and consent around personal data
Reputational risk if perceived as manipulative
Bias in who gets cultivated
Vendor data-sharing terms
The framework's path
High tier → full review with safeguards. Vendor data-terms vetting and privacy review (Pillar 4), a named owner (Pillar 7), and a risk-registry entry with monitoring.
A use case isn't only a story worth telling, it's something to learn from. The cards above show each one through the lens of risk and governance, responsible use. This lens adds the other half: responsible results, what actually happened to people, and what it teaches. Even a small example, summarizing emails, automating a form, carries real decisions about accuracy, privacy, and access. Use these five prompts to present, compare, and learn from any use case, and as a shared structure for a community of practice.
1
Purpose & context
What problem or goal prompted it, who it serves, and how it ties to mission.
2
What made it possible
The tools, data, access, skills, and conditions that enabled it.
3
People & process
Who was involved, what changed in their work, and how accuracy, privacy, and access were handled.
4
Outcomes & lessons
What actually happened, the benefits and surprises, and what you'd do differently.
5
Next steps
Whether to scale, refine, share, or retire, and what to watch.
A blank, fillable version of these five prompts, plus a mission & values check, ready to bring to a meeting or a community of practice.