# Assessment Redesign for Generative AI: Program-Level Assurance of Learning > Higher Education · ibl.ai > Source: https://ibl.ai/courses/higher-education/assessment-redesign-for-generative-ai > Last updated: 2026-11-14 *Program-Level Assurance of Learning* **A higher education course from ibl.ai (self-paced, 11 to 13 hours, free).** Assessment redesign for generative AI at program level: the two-lane model, TEQSA's three pathways, the AI Assessment Scale, interactive orals, detectors. - **Price:** Free - **Format:** Self-paced, online - **Time:** 11 to 13 hours - **Pace:** 3 hours per week - **Level:** Intermediate - **Language:** English - **Subject:** Teaching & Learning [Enroll](https://lms.ibl.ai/platform/highereducation/courses/course-v1:highereducation+assessment-redesign-for-generative-ai+self-paced) ## Course description Assessment redesign for generative AI has mostly happened one assignment at a time, and the surveys show it: 87 percent of United States faculty set their own AI rules while only 35 percent report a department rule and 48 percent an institutional one (Elon and AAC&U, January 2026), and only 20 percent of provosts describe their institution's approach as "primarily generative" (Inside Higher Ed, September 2026). This course teaches the program-level alternative. You will learn why Corbin, Dawson and Liu call course-by-course traffic-light schemes an "enforcement illusion," why detectors cannot carry the load when seven of them flagged non-native writers' TOEFL essays as AI-written 61.3 percent of the time on average, and how Sydney's two-lane model, TEQSA's three assurance pathways and the revised AI Assessment Scale fit together into one program map. You will build that map for a sample program, place secure points such as interactive orals where they assure the learning outcomes, audit a full set of assessment briefs with an AI agent, and leave with a rollout plan and its metrics. ### About this course Assessment redesign for generative AI has mostly happened one assignment at a time, and the surveys show it: 87 percent of United States faculty set their own AI rules while only 35 percent report a department rule and 48 percent an institutional one (Elon and AAC&U, January 2026), and only 20 percent of provosts describe their institution's approach as "primarily generative" (Inside Higher Ed, September 2026). This course teaches the program-level alternative. You will learn why Corbin, Dawson and Liu call course-by-course traffic-light schemes an "enforcement illusion," why detectors cannot carry the load when seven of them flagged non-native writers' TOEFL essays as AI-written 61.3 percent of the time on average, and how Sydney's two-lane model, TEQSA's three assurance pathways and the revised AI Assessment Scale fit together into one program map. You will build that map for a sample program, place secure points such as interactive orals where they assure the learning outcomes, audit a full set of assessment briefs with an AI agent, and leave with a rollout plan and its metrics. ### What you will be able to do - Distinguish discursive from structural assessment change, and classify their institution's current AI assessment rules as one or the other with evidence. - State the published false-positive and accuracy figures for AI-text detectors and explain, in a paragraph suitable for a faculty senate, why detection cannot serve as a program's control. - Map every assessment in one program onto Sydney's two lanes and the five levels of the revised AI Assessment Scale, and identify where the program has no secure assurance of a learning outcome. - Choose and justify one of TEQSA's three assurance pathways (whole-program, unit-level, hybrid) for a given program and describe what changes at each level. - Design an interactive oral assessment for one unit, including its rubric structure and staffing, drawing on the Griffith and Dublin City University case studies. - Run an AI agent over a full set of assessment briefs to produce a first-pass lane and level classification, verify it by hand, and use the result to write a rollout plan with measurable program-review metrics. ### Who this course is for **Roles:** provosts and vice provosts for academic affairs, directors of assessment and institutional effectiveness, quality-assurance and accreditation liaison officers, deans and department chairs, program directors, and center-for-teaching-and-learning leads who support program review. **Institutions:** universities and colleges of any size that run program review or assurance-of-learning processes, including those preparing accreditation self-studies. The two frameworks the course uses most, Sydney's two-lane model and TEQSA's pathways, are Australian; the course presents them as international models and asks learners to map them onto their own accreditor's expectations. **Before you start:** familiarity with your institution's program-review or assessment cycle. No statistics or coding is required. The lab uses an AI agent through a browser on a sample program's assessment briefs, or on the learner's own. ### Format - Estimated time: 11 to 13 hours. - Level: intermediate. Assumes program-review experience; assumes nothing about AI. - Hands-on: one lab that runs an AI agent over a supplied sample program (a 24-unit bachelor's program with 71 assessment briefs) or the learner's own briefs, four templates the learner keeps (program assessment map, secure-point register, interactive oral design, rollout plan), three discussions, and a capstone on one real program. - 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 taught here are expert guidance, not binding regulation, and the documents are still being revised. Dates are stated on every unit and unit 4.1.3 shows how to re-check them. ## Outline ### Section 0 · Start here - 0.1 Orientation ### Section 1 · Why course-by-course rules fail - 1.1 Where institutions stand - 1.2 Detection is not a control - 1.3 Section 1 assessment ### Section 2 · Program-level frameworks - 2.1 TEQSA: principles, propositions and pathways - 2.2 Sydney's two-lane model - 2.3 The AI Assessment Scale - 2.4 Section 2 assessment ### Section 3 · Secure assessment that scales - 3.1 Interactive oral assessment - 3.2 Open-lane assessment and the audit lab - 3.3 Section 3 assessment ### Section 4 · Governance, accreditation and rollout - 4.1 Making it stick - 4.2 Measuring the redesign - 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) - [Designing a Faculty AI Training Program: Frameworks, Tiers and Evaluation](https://ibl.ai/courses/higher-education/faculty-ai-training-program-design) - [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)