What is this course about?
This is a program you run rather than a course you take: six facilitated sessions with materials, artifacts, and a facilitator guide written for a room that contains genuine skeptics. It ends with each participant having redesigned one assignment and adopted one workflow, and with a measurement plan for what actually stuck six months later.
Who is this course for?
- Centers for teaching and learning staff who will facilitate
- Department chairs running local cohorts
- Faculty developers and instructional designers
- Faculty participants across disciplines
What do I need before starting?
- Facilitators should have run faculty development before
- Participants bring one course they currently teach
What will I be able to do afterwards?
- Facilitate a six-session AI literacy program with supplied materials
- Explain what current models do and do not do, accurately, to a skeptical colleague
- Redesign one assignment and adopt one grading or feedback workflow
- Discuss AI use with students in terms of disclosure rather than prohibition
- Measure durable adoption six months after the program ends
What does each module cover?
Session 1 — What do these models actually do?
90 minA working mental model of prediction, context, and limits, without mathematics or hype.
Objectives
- Describe next-token prediction in plain language
- Explain hallucination as a property rather than a bug
- Identify tasks that are structurally unsuited to a model
Topics
Activity. Deliberately induce a hallucination in your own discipline and analyze why it happened.
Session 2 — What skill actually transfers between tools?
90 minContext and framing rather than prompt tricks, so the skill survives the next model release.
Objectives
- Provide context that materially improves output
- Iterate on a response rather than restarting
- Recognize prompt advice that is superstition
Topics
Activity. Improve one weak output through three rounds of added context and document what worked.
Session 3 — What does this look like in my discipline?
90 minDiscipline-specific application using participants' own course materials.
Objectives
- Apply AI to a real task from your own teaching
- Evaluate output against disciplinary standards
- Identify where your discipline's norms constrain use
Topics
Activity. Apply AI to one real task from your course and critique the output against your standards.
Session 4 — Redesigning one assignment together
90 minThe working session where every participant converts one assignment with peer critique.
Objectives
- Convert one assignment so AI use does not undermine its evidence
- Give and receive structured peer critique
- Estimate the change in grading load
Topics
Activity. Convert your assignment and run it through structured peer critique.
Session 5 — Where does this save real hours?
90 minGrading, feedback, and course preparation — the workflows with a genuine time return.
Objectives
- Adopt one grading or feedback workflow
- Maintain feedback quality while reducing time
- Identify workflows that cost more time than they save
Topics
Activity. Run a real grading batch through your chosen workflow and time both approaches.
Session 6 — How do I talk to students about this?
90 minDisclosure norms, syllabus language, and the classroom conversation that sets expectations.
Objectives
- Explain your AI policy to students in terms they accept
- Model disclosure of your own AI use
- Handle the challenging student questions
Topics
Activity. Rehearse the first-day AI conversation with a colleague playing a challenging student.
Facilitator guide — running this with skeptics
60 minThe facilitation module: handling the room, the objections, and the colleague who came to argue.
Objectives
- Anticipate the four objections that reliably arise
- Distinguish productive skepticism from disruption
- Adjust pacing for a mixed-readiness room
Topics
Activity. Rehearse responses to the four standard objections with a co-facilitator.
Measuring what stuck six months later
45 minFollow-up measurement that distinguishes durable change from post-workshop enthusiasm.
Objectives
- Design a six-month follow-up measurement
- Collect artifacts rather than self-reported adoption
- Decide what to do about non-adopters
Topics
Activity. Design your follow-up protocol and schedule it before the program ends.
What is the capstone project?
Run one cohort and report what changed
Facilitate the full six-session program with a real departmental cohort, collect participant artifacts, and produce a six-month follow-up report on durable adoption with evidence rather than self-report.
Deliverable: A completed cohort with collected artifacts and a follow-up adoption report.
How are learners assessed?
- Participant artifacts — one converted assignment and one adopted workflow per person
- Facilitator observation against the guide's fidelity checklist
- Six-month follow-up evidence of durable use
What ships with the course?
Facilitator guide
Session-by-session running order, discussion prompts, and the questions that reliably derail a room.
Learner workbook
Exercises, checklists, and the templates each module's activity produces.
Hands-on lab environment
A sandboxed ibl.ai deployment so exercises run against real agents, not screenshots.
Assessment bank
Scenario questions and rubric criteria mapped to each stated learning outcome.
Source bibliography
Every primary regulation and standard cited on this page, linked and dated.
Which AI agents does this course use?
The hands-on modules run against agents already deployable on the ibl.ai platform for higher education.
Where does the course material come from?
Every module is grounded in primary sources — the regulation, standard, or research itself, not a summary of it. Each was resolved at authoring time.
- Office of Educational Technology
U.S. Department of Education
Federal guidance framing the program's policy sessions.
- AI and the Future of Teaching and Learning
U.S. Department of Education
Source for the human-in-the-loop principles taught in Sessions 4 and 5.
- EDUCAUSE
EDUCAUSE
Sector data on institutional AI adoption, used in the facilitator context material.
- AI Index Report
Stanford HAI
Capability baseline for the Session 1 mental model.
Delivery notes
Binding guidance for anyone preparing and delivering this course.
- The facilitator guide is the actual product. Write it for someone who has never used AI seriously and will face a hostile senior colleague in week one — include verbatim suggested responses, not principles.
- Session 1's induced-hallucination exercise is what converts skeptics, because it gives them a true thing to be skeptical about. Do not soften it into a demo of capability.
- Every session must produce a tangible artifact the participant keeps. Sessions without artifacts show near-zero six-month retention, which is exactly what Module 8 measures.
- Do not schedule this in a single day. The six-week spacing is load-bearing — the between-session application is where the learning happens.
- Include a genuinely negative example in Session 5. Faculty trust the program more once it names a workflow that wastes time, and there are several.
Why run AI training on a platform you own?
You own the course, not a licence to it
Course content, learner data, and the platform run inside your perimeter — you own all the code and the data.
Model-agnostic delivery
Run the course's AI components on any LLM — Claude, GPT, Llama, Gemini, Command — and switch anytime.
No per-seat training licences
Usage-based or self-hosted, so cost tracks actual use rather than headcount.
Deploy anywhere
Cloud, private VPC, on-premise, or fully air-gapped — including for cohorts that cannot use public AI tools.
Frequently asked questions
What does the Faculty AI Literacy: A Six-Week Teach-the-Teacher Program course cover?
This is a program you run rather than a course you take: six facilitated sessions with materials, artifacts, and a facilitator guide written for a room that contains genuine skeptics. It ends with each participant having redesigned one assignment and adopted one workflow, and with a measurement plan for what actually stuck six months later. It runs 6 sessions of 90 minutes, run over six weeks across 8 modules, at foundational level, and closes with a capstone: Run one cohort and report what changed.
Who should take Faculty AI Literacy: A Six-Week Teach-the-Teacher Program?
It is written for Centers for teaching and learning staff who will facilitate, Department chairs running local cohorts, Faculty developers and instructional designers, Faculty participants across disciplines. Prerequisites: Facilitators should have run faculty development before; Participants bring one course they currently teach.
Can we run this course on our own infrastructure?
Yes. ibl.ai is model-agnostic and deploy-anywhere — cloud, private VPC, on-premise, or fully air-gapped — and you own all the code and the data. Cohort data, submissions, and any material learners upload stay inside your perimeter, which matters for higher education teams that cannot send work to a public AI tool.
How do we get access to Faculty AI Literacy: A Six-Week Teach-the-Teacher Program?
Request access and we will set it up for your cohort — hosted by ibl.ai, or running against your own deployment. Tell us the group size and timing you need, and whether it should run inside your own perimeter.
How much does AI training for higher education cost on ibl.ai?
There is no per-seat pricing — you pay for usage or self-host and pay only for the infrastructure, so a 5,000-person rollout does not cost 5,000 licences. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.