What question did this study set out to answer?

The main aim is to develop an adaptive learning engine that uses confidence judgments to enhance exam preparation.

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March 3, 2026Open Access

Confidence-Calibrated Adaptive Learning: An Integrated Adaptive Engine for Professional Exam Preparation

Key Points

The main aim is to develop an adaptive learning engine that uses confidence judgments to enhance exam preparation.
Developed an integrated adaptive engine with six subsystems
Applied across 20 production applications in four professional domains
Included over 55,000 exam items for diverse learner engagement
Enhanced learning outcomes through asymmetric scoring and spaced repetition
Improved misconception detection with a focus on high-confidence errors
Provided accurate readiness predictions aligned with Bloom's taxonomy

Abstract

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.

Confidence-Calibrated Adaptive Learning: An Integrated Adaptive Engine for Professional Exam Preparation

Key Points

Abstract

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