# Designing a Faculty AI Training Program: Frameworks, Tiers and Evaluation > Higher Education · ibl.ai > Source: https://ibl.ai/courses/higher-education/faculty-ai-training-program-design > Last updated: 2026-11-14 *Frameworks, Tiers and Evaluation* **A higher education course from ibl.ai (self-paced, 10 to 12 hours, free).** Design a faculty AI training program: map competencies to EDUCAUSE, UNESCO and DOL frameworks, tier it, govern it with faculty and evaluate it. - **Price:** Free - **Format:** Self-paced, online - **Time:** 10 to 12 hours - **Pace:** 3 hours per week - **Level:** Intermediate - **Language:** English - **Subject:** Teaching & Learning [Enroll](https://lms.ibl.ai/platform/highereducation/courses/course-v1:highereducation+faculty-ai-training-program-design+self-paced) ## Course description Faculty AI training is now the stated need of most instructors and the stated policy of few institutions: in the Digital Education Council's 2025 global survey of 1,681 faculty, 61 percent had used AI in teaching, 80 percent found their institution's guidelines not comprehensive, and only 6 percent were fully satisfied with the AI literacy resources available to them. This course is for the people who have to close that gap with a program, not a workshop. You will map competencies to three public frameworks (EDUCAUSE's AI literacy framework for teaching and learning, UNESCO's competency framework for teachers, and the Department of Labor's 2026 AI literacy framework), structure the program in UNESCO's three levels (Acquire, Deepen, Create), build discipline tracks to fill the gap Ithaka S+R documented, run the program through shared governance as the AAUP recommends, and evaluate it against a baseline you set on day one. You will leave with a competency map, a tiered program design, a workshop plan built around a hands-on agent exercise, a committee charter and an evaluation plan. ### About this course Faculty AI training is now the stated need of most instructors and the stated policy of few institutions: in the Digital Education Council's 2025 global survey of 1,681 faculty, 61 percent had used AI in teaching, 80 percent found their institution's guidelines not comprehensive, and only 6 percent were fully satisfied with the AI literacy resources available to them. This course is for the people who have to close that gap with a program, not a workshop. You will map competencies to three public frameworks (EDUCAUSE's AI literacy framework for teaching and learning, UNESCO's competency framework for teachers, and the Department of Labor's 2026 AI literacy framework), structure the program in UNESCO's three levels (Acquire, Deepen, Create), build discipline tracks to fill the gap Ithaka S+R documented, run the program through shared governance as the AAUP recommends, and evaluate it against a baseline you set on day one. You will leave with a competency map, a tiered program design, a workshop plan built around a hands-on agent exercise, a committee charter and an evaluation plan. ### What you will be able to do - Report a faculty AI baseline for their institution using survey items adapted from the Digital Education Council, Tyton Partners and Ithaka S+R studies, and state the gap between faculty and administrator perceptions in their own numbers. - Crosswalk the EDUCAUSE, UNESCO and Department of Labor frameworks into one competency map with the source framework cited for every competency. - Design a three-tier program (Acquire, Deepen, Create) with entry criteria, completion evidence and at least two discipline tracks per tier. - Build and run a 90-minute faculty workshop centered on a hands-on exercise in which participants configure and critique an AI agent on sample course materials. - Draft a charter for a faculty-led AI professional-development committee that satisfies the AAUP's shared-governance recommendation and connects to the institution's AI policy body. - Write an evaluation plan with participation, competency-movement and classroom-practice measures, and a re-survey schedule that closes the perception gap over time. ### Who this course is for **Roles:** directors and staff of centers for teaching and learning; faculty-development and instructional-design leads; associate provosts for faculty affairs or academic innovation; faculty senate and union representatives on AI committees; academic technology directors who fund and host the program. **Institutions:** four-year universities, community colleges (the Department of Labor framework is addressed in part to them), and systems designing a shared program across campuses. The design also applies to health-professions schools and professional schools that need discipline tracks. **Before you start:** experience running any faculty-development offering (a workshop series, a learning community, a certificate) and familiarity with how your institution approves professional development. No technical background; the lab uses a hosted agent and sample course materials. ### Format - Estimated time: 10 to 12 hours. - Level: intermediate practitioner. - Hands-on: one needs-assessment worked example, one framework crosswalk worked example, one AI-agent lab that faculty developers run first as participants and then as facilitators, four templates (competency map, tier design, workshop plan, committee charter), two discussions, and a capstone. - Structure: 4 sections, each ending in an AI conversation assessment, then a capstone assessment. Self-paced. Every lesson is taught by an AI tutor that opens with a short story and works through one idea and one question at a time. Each section ends with an AI conversation assessment, and the course closes with a capstone assessment. Section assessments count for 60% and the capstone for 40%; 70% passes. There are no multiple-choice quizzes. ### Currency of this course Last verified 2026-09-28. The frameworks this course maps to are still moving: the U.S. Department of Labor's AI literacy framework (TEN 07-25) is voluntary guidance issued 2026-02-13, the EU AI Act's Article 4 duty was amended in 2026 and the amendment is secondary-sourced here, and the survey figures cited are from 2025 and 2026 editions that are re-fielded annually. Re-check the reading list before relying on any figure. ## Outline ### Section 0 · Start here - 0.1 Orientation ### Section 1 · The case and the baseline - 1.1 What faculty say they need - 1.2 Running a baseline at your institution - 1.3 Section 1 assessment ### Section 2 · Frameworks to map competencies - 2.1 The higher-education and teacher frameworks - 2.2 The workforce and policy frameworks - 2.3 Section 2 assessment ### Section 3 · Designing the tiered program - 3.1 Tiers and tracks - 3.2 The workshop and the hands-on core - 3.3 Section 3 assessment ### Section 4 · Governance and evaluation - 4.1 Shared governance - 4.2 Evaluation - 4.3 Section 4 assessment ### Section 5 · Capstone & wrap-up - 5.1 Capstone - 5.2 Capstone assessment - 5.3 Wrap-up ## Instructors - ibl.ai Higher Education, Course team (ibl.ai) ## More higher education courses - [AI Tutoring Research: What the RCTs Show and How to Deploy a Tutor That Improves Learning](https://ibl.ai/courses/higher-education/ai-tutoring-research-what-rcts-show) - [Assessment Redesign for Generative AI: Program-Level Assurance of Learning](https://ibl.ai/courses/higher-education/assessment-redesign-for-generative-ai) - [ADA Title II Digital Accessibility for Course Content: The 2027 Deadline](https://ibl.ai/courses/higher-education/ada-title-ii-course-accessibility) - [AI Academic Advising at Scale: Caseload Triage, Proactive Outreach and Human Handoffs](https://ibl.ai/courses/higher-education/ai-academic-advising-caseload-management)