The Higher Ed AI Field Guide
Field guides for higher education leaders trying to move from AI activity to institutional readiness.
The Higher Ed AI Field Guide is a growing library of field guides on what AI readiness actually looks like inside colleges and universities. Each guide stands on its own — one topic, one part of institutional readiness, from governance and policy to teaching, student success, operations, finance, and culture.
The premise is simple: AI readiness is not just a tool question. It is a question of institutional capacity.
Browse by area
Every guide covers one topic you can act on. Find the area you're working in and start there.
Governance & policy
The Committee Is Not the Strategy
· 6 min read
Committees matter, but a committee without authority, decision rights, escalation paths, and an operating rhythm becomes a place where AI work gets observed rather than led.
Read the guideThe Policy Nobody Read
· 5 min read
A policy that lives in a PDF and covers only the classroom is not enough. The real question is whether faculty, staff, and students know what they can and cannot do, and whether those rules are usable in daily work.
Read the guideBoards Need More Than an AI Risk Briefing
Boards need to understand AI as risk, but also as a question of institutional relevance, financial sustainability, academic value, and public trust.
PreviewData, risk & compliance
Shadow AI Is Already Your Operating Model
· 5 min read
Staff and faculty are already using AI to survive their workloads. The question is whether the institution can turn hidden improvisation into responsible capacity.
Read the guideThe Risk You Can't See Yet
· 5 min read
AI introduces new risks that may not show up in traditional security planning. Readiness means treating AI risk as an ongoing operating concern, not a one-time approval gate.
Read the guideWho Checks Whether the Model Is Fair?
· 5 min read
Equity is not a value statement at the end of an AI strategy. It is an audit function with an owner, a process, and a pause-and-fix protocol.
Read the guideFERPA Was Written for a Different Machine
FERPA, accessibility, accreditation, copyright, procurement, and employment obligations all apply to AI in ways many institutions have not fully mapped.
PreviewWhat to Ask Before Signing the Next AI Contract
Many consequential AI decisions arrive through vendor contracts or embedded features in systems the institution already owns. Readiness starts before procurement, not after.
PreviewTeaching & research
What Teaching Looks Like When AI Is Ambient
This is not only about banning or embracing tools. It is about what teaching, AI literacy, and course design become when AI is part of the room.
PreviewAcademic Integrity Was the Doorway, Not the Room
Academic integrity opened the conversation, but the larger question is how assessment changes when AI can participate in many forms of student work.
PreviewThe Work That Changes: Augmentation and Anxiety
AI literacy is labor. Faculty and staff need time, examples, incentives, and support, not just another expectation to absorb.
PreviewWho Gets Credit When the Co-Author Is a Model?
AI in research is not only about speed. It is about methods, disclosure, integrity, access, data handling, and what counts as scholarly work.
PreviewWhen the Tool Stops Waiting for a Prompt
Agentic AI changes the question from what people do with AI to what AI systems are allowed to do with, for, and around people.
PreviewStudents
The Student Support Maze Is Getting an AI Layer
AI readiness should be judged by whether it helps students navigate the institution, not by whether the demo is impressive.
PreviewThe First Institution to Respond Usually Wins
Prospective students may experience institutional AI before they ever enroll. Public information quality, inquiry routing, and trust matter early.
PreviewFrom First Inquiry to Alumni, One Continuous Line
Students do not experience institutions as org charts. AI readiness has to account for handoffs across admissions, advising, registration, career, alumni, and beyond.
PreviewThe Overlooked AI Opportunity in the Development Office
AI readiness does not stop at graduation. Alumni engagement, donor trust, personalization, privacy, and stewardship are part of the map.
PreviewOperations & finance
Where AI Creates Capacity, and Where It Creates Hidden Labor
Much of AI's value may show up in unglamorous workflows. Readiness starts with the work, not the tool.
PreviewYou Can't Run AI on Disconnected Systems
AI strategy eventually meets the data stack, identity system, integration backlog, support model, and the people who know how everything actually works.
PreviewDoes Every AI Initiative Have a Path to ROI?
The AI budget is not only the license cost. Training, integration, governance, accessibility, support, evaluation, and sustainability all count.
PreviewCulture & what's next
What Can't Be Replicated: Identity in an AI-Shaped Market
AI adoption is partly technical, partly cultural, and often a communications problem wearing a hoodie. Institutions need language people can trust.
PreviewAI Readiness Doesn't Stop at the Campus Edge
Institutions also have to decide how they show up for employers, schools, civic partners, and the communities they serve.
PreviewWhat's Coming, and What We Start Now
Readiness is not a one-time assessment. Institutions need structures for questions that have not fully arrived yet.
PreviewGet the next Field Guide
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