Cross-Domain Analysis of a Confidence-Calibrated Adaptive Learning Engine

When a single adaptive engine serves 22 apps across four certification domains, which domains concentrate misconceptions on hard items, and can this be predicted without learner data? Across 35,250 production items, score-level differentiation (4.5× misconception ratio, d ≥ 1.99) is domain-invariant by design, but item-level concentration depends on a two-factor interaction between difficulty spread (σ) and base accuracy (b_d). When domains share b_d = 0.72, wider σ drives 69% more hard-item misconceptions; when σ is nearly matched, a one-point b_d difference shifts the ordering. A predictor P(d_i > 0.6) × (1 − b_d) ranks all four domains correctly across a 6× sensitivity sweep, enabling domain-aware scheduling from item bank statistics alone. Submitted to EDM 2026 Poster/Demo Track.