# Faculty AI Literacy: A Six-Week Teach-the-Teacher Program > Higher Education · AI Course · HE-10 > Source: https://ibl.ai/solutions/higher-education/course/faculty-ai-literacy-program > Last updated: 2026-08-25 **A ready-to-run faculty development program — six sessions, artifacts, and a facilitator guide — that moves a department from anxiety to designed AI use.** ## The Short Answer **Faculty AI adoption fails when training is a one-off workshop rather than a facilitated program with artifacts. ibl.ai supplies both the six-session program and the platform it runs on — self-hosted, model-agnostic, and yours, because you own all the code and the data, so faculty can experiment with real course materials without sending them to a public model.** On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. [Request Access](https://ibl.ai/contact) · [Explore Higher Education](https://ibl.ai/solutions/higher-education) ## Course facts - **Level:** Foundational - **Duration:** 6 sessions of 90 minutes, run over six weeks - **Format:** Facilitated program with a facilitator guide and participant artifacts - **Modules:** 8 - **Catalog code:** HE-10 - **Frameworks covered:** NIST AI RMF, Institutional academic integrity policy ## 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? ### Module 1 — Session 1 — What do these models actually do? A working mental model of prediction, context, and limits, without mathematics or hype. _(90 min)_ **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:** Prediction and context · Hallucination as a property · Capability boundaries · Common misconceptions **Activity:** Deliberately induce a hallucination in your own discipline and analyze why it happened. ### Module 2 — Session 2 — What skill actually transfers between tools? Context and framing rather than prompt tricks, so the skill survives the next model release. _(90 min)_ **Objectives** - Provide context that materially improves output - Iterate on a response rather than restarting - Recognize prompt advice that is superstition **Topics:** Context provision · Iterative refinement · Task decomposition · Prompt superstition **Activity:** Improve one weak output through three rounds of added context and document what worked. ### Module 3 — Session 3 — What does this look like in my discipline? Discipline-specific application using participants' own course materials. _(90 min)_ **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:** Disciplinary application · Standards-based evaluation · Field-specific norms · Peer comparison **Activity:** Apply AI to one real task from your course and critique the output against your standards. ### Module 4 — Session 4 — Redesigning one assignment together The working session where every participant converts one assignment with peer critique. _(90 min)_ **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:** Assignment conversion · Peer critique protocol · Grading load · Rubric adjustment **Activity:** Convert your assignment and run it through structured peer critique. ### Module 5 — Session 5 — Where does this save real hours? Grading, feedback, and course preparation — the workflows with a genuine time return. _(90 min)_ **Objectives** - Adopt one grading or feedback workflow - Maintain feedback quality while reducing time - Identify workflows that cost more time than they save **Topics:** Feedback workflows · Grading support · Course preparation · Negative-return workflows **Activity:** Run a real grading batch through your chosen workflow and time both approaches. ### Module 6 — Session 6 — How do I talk to students about this? Disclosure norms, syllabus language, and the classroom conversation that sets expectations. _(90 min)_ **Objectives** - Explain your AI policy to students in terms they accept - Model disclosure of your own AI use - Handle the challenging student questions **Topics:** Student conversation · Disclosure modeling · Syllabus communication · Difficult questions **Activity:** Rehearse the first-day AI conversation with a colleague playing a challenging student. ### Module 7 — Facilitator guide — running this with skeptics The facilitation module: handling the room, the objections, and the colleague who came to argue. _(60 min)_ **Objectives** - Anticipate the four objections that reliably arise - Distinguish productive skepticism from disruption - Adjust pacing for a mixed-readiness room **Topics:** Common objections · Productive skepticism · Pacing adjustment · Room management **Activity:** Rehearse responses to the four standard objections with a co-facilitator. ### Module 8 — Measuring what stuck six months later Follow-up measurement that distinguishes durable change from post-workshop enthusiasm. _(45 min)_ **Objectives** - Design a six-month follow-up measurement - Collect artifacts rather than self-reported adoption - Decide what to do about non-adopters **Topics:** Follow-up design · Artifact collection · Self-report limits · Non-adopter response **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? - [Faculty Agent](https://ibl.ai/solutions/higher-education/agent/faculty-agent) - [Tutoring Agent](https://ibl.ai/solutions/higher-education/agent/tutoring-agent) - [Research Agent](https://ibl.ai/solutions/higher-education/agent/research-agent) - [Administrative Agent](https://ibl.ai/solutions/higher-education/agent/administrative-agent) ## 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](https://tech.ed.gov/) — U.S. Department of Education. Federal guidance framing the program's policy sessions. - [AI and the Future of Teaching and Learning](https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf) — U.S. Department of Education. Source for the human-in-the-loop principles taught in Sessions 4 and 5. - [EDUCAUSE](https://www.educause.edu/) — EDUCAUSE. Sector data on institutional AI adoption, used in the facilitator context material. - [AI Index Report](https://hai.stanford.edu/ai-index) — 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. ## More Higher Education courses - [FERPA-Compliant AI: Deploying Agents on Student Data](https://ibl.ai/solutions/higher-education/course/ferpa-compliant-ai): Run AI agents against your SIS and LMS without a vendor ever seeing a student record — the school official exception, vendor DPAs, and the architecture FERPA implies. - [AI Academic Advising at Scale: Design and Guardrails](https://ibl.ai/solutions/higher-education/course/ai-academic-advising-at-scale): Build an advising agent that handles degree audits and registration at 20,000-student scale without ever giving a student wrong graduation advice. - [Enrollment and Yield AI: Agents Across the Funnel](https://ibl.ai/solutions/higher-education/course/enrollment-and-yield-ai): Deploy AI across inquiry, application, admit, and melt — where agents lift yield, where they damage trust, and how to keep the funnel on infrastructure you own. - [AI Tutoring That Improves Outcomes, Not Just Engagement](https://ibl.ai/solutions/higher-education/course/ai-tutoring-that-improves-outcomes): Design a tutoring agent that produces measurable learning gains — Socratic scaffolding, answer-withholding, misconception detection, and honest outcome measurement. - [Assessment Redesign for the AI Era](https://ibl.ai/solutions/higher-education/course/assessment-redesign-for-the-ai-era): Detection does not work. Rebuild assessment around what AI cannot fake — process, oral defense, local context, and in-class artifacts — with department-ready rubrics. - [Writing a Campus AI Policy That Survives Accreditation](https://ibl.ai/solutions/higher-education/course/campus-ai-policy-that-survives-accreditation): Draft institutional AI policy a regional accreditor, a general counsel, and a faculty senate will each accept — with the governance to keep it current.