A board-by-board visual of a 10-week, portfolio-driven professional course, designed from Course Outcome to Learning Experience.
We open on the whole course: six measurable Course Learning Outcomes and the refined 10-week architecture that carries the learner from AI literacy to enterprise leadership.
Each CLO begins with a measurable verb and reflects workplace capability, not knowledge recall. Collectively they span foundations, risk, governance, compliance, assurance, and readiness.
The original 12 suggested nodes were consolidated into 10 modules with the capstone embedded in Week 10, producing a cleaner instructional progression: the learner first understands the AI system, then manages its risk, governs and complies with it, assures and responds to incidents, and finally leads the workforce. This follows a cognitively sound path from comprehension to application to leadership (Bloom's higher-order; Fink's integration).
Module 6 is developed in full depth in Acts II–IV. The capstone integrates the portfolio and workforce-readiness outcomes.
The camera moves in on a single module, developed completely: a role-based scenario where the learner becomes the AI Risk Manager at Meridian Regional Bank.
Four measurable MLOs for Module 6, each aligned directly to the Course Learning Outcomes above. The alignment chain is the spine of the design.
Every MLO maps to a CLO, ensuring the module contributes measurably to course-level capability.
Portfolio alignment: MLO3 & MLO4 → CLO2, CLO3, CLO6.
The full 13.5-hour week, built for a working professional and sequenced as a story arc: Acquire → Explore → Practice → Apply / Demonstrate.
Time is deliberately non-uniform, justified by content complexity and application effort rather than equal allocation.
"Acquisition is capped because mid-career learners are time-poor and learn by doing; Cognitive Load Theory argues against overloading the working professional with passive content. The heaviest investment is Practice/Apply/Demonstrate (8.5h), reflecting the workforce-ready, portfolio-driven intent and SDT's competence/autonomy needs."
All resources are openly accessible and WCAG 2.1 AA-compliant where hosted; instructor media carry captions and transcripts.
Meridian Regional Bank: AI Risk Decision Lab. You are the AI Risk Manager at Meridian Regional Bank. Two production systems are in scope: (a) an automated credit-decisioning model and (b) a generative-AI customer-service assistant. A recent regulatory inquiry asks the bank to evidence its AI risk process within 30 days.
Expected output: a draft AI risk register (min. 6 scored, treated, owned risks) plus a reflective paragraph. This output feeds directly into the portfolio assignment and evidences MLO1–MLO3. Document: AI_Risk_Decision_Lab_Worksheet.
Formative, scenario-based, with immediate feedback. Eight MCQs + one decision task; sample items below.
Facilitated synchronously; recorded with captions and offered as an async alternative (UDL).
Summative: Enterprise AI Risk Register & Treatment Brief. Learners finalize the decision-lab draft into a submission-grade artifact: a scored register of at least 8 risks (owned, treated, residual-rated) plus a 1-page treatment brief. Alignment: MLO3 & MLO4 → CLO2, CLO3, CLO6.
The frameworks that justify the design decisions across Acts I–IV, demonstrating the instructional judgment expected of an independent curriculum designer.
CIPP model frames the design and its evaluation plan.
Context — mid-career, online, workforce-ready audience with limited weekly time.
Input — 13.5h/week budget, NIST/MITRE/ISO frameworks, Rise + live delivery.
Process — iterative Rapid Prototyping of the module before full-course rollout.
Product — portfolio artifact graded by a transparent rubric.
Formative: expert review → one-to-one learner test → small-group pilot → field trial, each informing revision. Confirmative: post-launch check that graduates apply the portfolio artifact in their workplace roles.