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An adaptive learning engine improves exam performance through confidence-based systems in professional domains, suggesting effectiveness.
We present an adaptive learning engine that elevates binary confidence judgments to a primary adaptive signal driving six interdependent subsystems: asymmetric scoring, confidence-modulated spaced repetition, misconception detection via high-confidence errors, review prioritization, multi-factor readiness prediction, and Bloom's-aligned assessment. The engine is deployed across 20 production applications in four professional domains with over 55,000 items. Submitted to AIED ?2026 Late Breaking Results.
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