Assess graduate students' ability to evaluate national AI governance frameworks by applying multi-criteria decision analysis (MCDA) under uncertainty to balance innovation, privacy, security, and equity. Expect demonstration of MCDA method selection (e.g., AHP, TOPSIS, robust MCDA), criteria identification and operationalization, justification of weighting under epistemic and aleatory uncertainty, development of evaluation matrices, scenario and sensitivity analyses, stakeholder trade-off articulation, equity and rights-based impact assessment, and formulation of adaptive, evidence-based policy recommendations supported by robustness checks and case-study critique.