Your campus paid for the AI tools, but the best prompts are stuck in private chat histories while every department rebuilds theirs from scratch. There are four steps that will turn these siloed experiments into a shared AI resource library your faculty can use every week.
Most colleges that rolled out generative AI tools also set up a place for faculty to share prompts. It might be a SharePoint site, a Teams channel, or a page in the LMS. In most cases, very few faculty use it because they can’t tell if an entry is safe to use with course material, or if adding their own context will take time they don't have. So, useful prompts stay in personal chat histories, and people in different departments end up writing the same ones on their own.
Here are 4 steps to build a shared AI resource library that faculty will use, trust, and add to.
1. Find What’s Working
Most library projects start with the platform. Someone picks a tool, builds a folder structure, and sends an invitation to contribute. Faculty open it, find it empty, and don't come back.
Start with the content instead. Faculty on your campus are already using AI to write rubrics, draft feedback, build quiz questions, and plan lessons. A few of them have prompts they've refined over an entire semester. Those prompts belong at the center of your shared AI resource library.
How to Find the Faculty Already Using AI Well
You likely have more evidence than you think. Look at these sources first:
Help desk tickets and tool requests. People asking detailed questions about a tool are usually using it every week.
Training attendance. Pay attention to anyone who came back for a second or third session.
Department chairs and instructional designers. They usually know who's experimenting in their area.
Teaching and learning center staff. Faculty often mention their AI work during consultations.
Once you have names, reach out directly. A personal request from a dean or chair gets a much better response than a campus-wide email.
Run Short "Show Me Your Best Prompt" Sessions
Hold 30 minute sessions by department. Ask each person to bring one prompt that saved them time and show what it produced. Keep the tone informal and collaborative, since no one is being graded.
Take notes or record the session. Many faculty won't write up their own prompts, but they'll gladly show one in a meeting. Your team or an instructional designer can write the entry afterward and send it back to the faculty member for a quick check.
What to Collect From Each Prompt
For every prompt, capture:
The teaching task it solved
The prompt text exactly as the person uses it
A sample of the output
What the person checks or changes before using the output
Which AI tool they used
Whether any student or course data went into it
Ask about data directly. If someone has been pasting student work into an unapproved tool, you want to know now, before that prompt ends up in a shared space. Flag those entries for review in the next step.
2. Set a Quality Bar So Nobody Gets Burned by a Bad Prompt
One bad experience can end someone's use of the library. A faculty member tries a prompt, it produces citations that don't exist or feedback that sounds nothing like them, and they decide the whole collection can't be trusted. A clear standard for every entry will prevent most of those moments.
Use One Template for Every Entry
Reusable AI prompts for teachers only work if the next person can understand them without asking the author. When every entry follows the same format, faculty can scan it in seconds and know whether it fits their needs. A simple template covers:
Task. What the prompt helps with, in plain terms, such as "draft rubric criteria for a lab report."
Context. Course level, discipline, and any assumptions the prompt makes.
Prompt. The full text, with clear placeholders for the parts a user should change.
What to check. The specific things to review in the output before using it.
Approved tool. Which campus tool the prompt was tested in.
Data rules. What can and can't go into the prompt.
Contributor and date. Who wrote it and when it was last reviewed.
The "what to check" field matters most. It keeps faculty judgment in the process and tells newer users where AI output tends to go wrong for that task.
Spell Out the Data Rules in Plain Language
Faculty shouldn't need to read a policy document to know whether a prompt is safe. Put the answer on the entry itself. Use short labels such as "No student names or grades," "Fine for published course materials," or "Anonymized data only." Tie each label to your existing AI policy so the rules match what faculty hear in training and from their deans. This is where AI governance in higher education becomes something people can follow when they feel busy and short on time.
Split Review Between Faculty and IT
Give each department one or two faculty stewards who review new entries for teaching quality. They can answer the question IT can't: would a good instructor use this output?
IT handles the other half. Your team confirms the tool is on the list of approved AI tools for faculty, checks the data rules, and flags anything that raises FERPA or other compliance concerns. Keep the review light. A steward should be able to approve an entry in about ten minutes; if review takes weeks, people will stop submitting.
Date Every Entry and Retire Old Ones
AI tools change often, and a prompt that worked last spring may behave differently now. Show the last review date on every entry. Set a reminder to recheck each one at least once a year, and archive entries that no longer work. Faculty will trust a smaller library of current entries far more than a large one full of outdated material.
3. Put the Library Where Faculty Already Work Every Day
Faculty spend most of their working hours in a few places: the LMS, email, the faculty portal, and whatever they use to build course materials. A library that lives anywhere else asks them to remember it exists, find the link, and log in. Most won't. Whether you call it a library or a campus AI resource hub, faculty will use it more when it shows up inside the tools they already open every day.
Bring the Library Into the LMS
Add a link or embedded page to your LMS course template so it appears in every course by default. Many LMS platforms also support a global navigation item, which puts the library one click away from any course page. Add links in the faculty portal, on the teaching and learning center site, and in the signature of your AI support emails.
Timing matters as well. Faculty look for help when they're building a syllabus, writing an assignment, or facing a stack of papers to grade. Point to relevant entries in the places they visit during those moments, such as assignment setup pages and grading guides.
Organize Entries by Teaching Task
Faculty search by the job in front of them. They want help writing feedback on essays, building a rubric, or creating practice questions. Few of them start by thinking about which AI tool to use. Build your top categories around those jobs:
Course design and syllabus planning
Lesson and lecture preparation
Assignment and assessment design
Rubrics and grading support
Student feedback
Accessibility and accommodations
Research and scholarly writing
Under each category, add filters for discipline and course level so a chemistry instructor isn't scrolling past composition prompts. Include a tool tag as well, so people can still find entries for a specific platform. If you're wondering how to organize an AI prompt library, this is the simplest answer: match the structure to the work.
Write Tags the Way Faculty Talk
IT staff and faculty often use different words for the same thing. Your team might tag an entry "content generation," while a professor searches for "quiz questions." Review search logs every few weeks to see what people type and whether they found anything. Add those terms as tags. When a search returns nothing, treat it as a request for a new entry.
Create a Start Here Collection
New users need a smaller, guided entry point. Pick 8-10 simple, reliable prompts that work across disciplines, with short notes on how to adapt each one. Include this collection in new faculty orientation and adjunct onboarding. Adjuncts often have the least time and the least support, so a short, trusted set of prompts gives them a practical place to begin and makes AI knowledge sharing for educators part of their first week on campus.
4. Make Sharing Feel Like Recognition Instead of Homework
The first three steps get people using the library. This step keeps them adding to it. Faculty contribute when it's easy, when someone notices, and when it counts toward something they already care about. If you're wondering how to get faculty to share AI prompts, build around those three conditions.
Give Stewards Necessary Time and Meaningful Credit
Department stewards keep the library useful, so treat the role as a service that matters. Work with the provost and deans to count stewardship toward service expectations. Offer a small stipend or course release where the budget allows, and list stewards by name on the library's home page. A role with no time or credit attached will sit empty within a year.
Make Contributors Visible
Put contributor names on every entry, and then go further. Feature a prompt of the month in faculty newsletters, with a short note from the author about how they use it. Ask deans to mention useful entries in department meetings. Send authors a short update each term showing how many colleagues reused their prompts. Learning that dozens of people used your feedback prompt is a strong reason to share the next one.
Consider adding library contributions to teaching award criteria and annual review documentation. Faculty can then point to their entries as evidence of teaching innovation.
Ask for Small Contributions on a Predictable Schedule
A broad request to "share your AI work" rarely gets a response. A specific, small request does. Twice a semester, ask faculty for one prompt that saved them an hour. Time the request for natural moments, such as the week after midterms or the end of the term, when people have a fresh example in mind.
Offer three easy ways to contribute: a short form, a 15 minute conversation with a steward, or forwarding the prompt by email. Let your team handle the formatting.
Build contributions into faculty AI training and professional development as well. End each workshop by asking participants to submit one prompt they built during the session, and have stewards review those submissions the same week.
Measure Reuse Instead of Page Visits
Page views only show that someone opened the library. To see whether it's changing how faculty work, track these measures:
How often entries are copied, saved, or downloaded
How many entries faculty adapt and resubmit for their own courses
Search terms that return no results, which point to missing content
Requests for new entries or help from stewards
Active contributors by department
Report these numbers to deans and the provost each term. Measuring AI adoption beyond logins gives leadership clear evidence of changing practice, and it shows your team where to focus next.
A shared AI resource library works when faculty can find it, trust what's in it, and see that their contributions count. Each of the four steps addresses one of the reasons most libraries sit unused. You start with prompts that already work, hold every entry to a clear standard, put the library where faculty spend their day, and give contributors real credit for sharing.
Most campuses can do this with the platforms they already own. What holds a library back is the lack of a shared way of working. Entries look different in every department, reviewers disagree about what counts as a good prompt, and faculty aren't sure what's safe to share. When that happens, people go back to asking the colleague down the hall.
The fix starts with common ground. Faculty, stewards, and IT need one template for writing prompts, one standard for judging AI output, and one set of data rules that says what’s safe to share. With that in place, each new entry is easier for faculty to write, for stewards to review, and for colleagues to reuse.
We help colleges and universities build that common ground through AI training and workshops designed around your faculty, approved tools, and policies. Faculty leave with a structured way to build prompts they can reuse and teach to colleagues. Your stewards and IT team get clear standards for reviewing entries, so the library stays trustworthy as it grows.
Book a conversation with us to talk through AI training options and workshops built for your institution. We’ll look at how your faculty use AI today and what they’d need to start using, and adding to, a library they trust.