Assess the ability to analyze sources of algorithmic bias and evidence of validity in automated student assessment and recommendation systems in higher education; tasks include critiquing dataset representativeness, model design, fairness metrics, construct and criterion validity, validation study design, impact on student outcomes, and proposing mitigation, evaluation, and governance strategies grounded in ethics and policy.