Confidence-Calibrated Adaptive Learning: An Integrated Adaptive Engine for Professional Exam Preparation
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.
Fields of Study & Subjects
Intelligent Tutoring Systems and Adaptive Learning
Online Learning and Analytics
Educational Technology and Assessment
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 1 March 2026
- Language
- EN
- Document type
- Preprint