Uncertainty-Aware Curriculum Models for High-Stakes Decisions

Abstract

This review examines uncertainty-aware curriculum models across financial and neural decision systems through an evidence-centered design lens. The analysis asks when uncertainty should trigger correction or escalation. It treats the relevant unit as a complete pathway from data or physical observations to representation, model output, human interpretation, and accountable action. The cited literature is synthesized without inventing experiments or unreported performance values. Particular attention is given to confident outputs being acted on outside their evidence boundary. The review argues that credible translation requires explicit evidence boundaries, uncertainty-aware evaluation, author-visible traceability, and a documented route for intervention. The resulting framework supports fraud assessment and assistive communication while distinguishing component promise from system readiness.