The misconception library
Some wrong answers are not gaps — they are confident, specific wrong models. Maestro builds a per-course registry of them, binds them to the lessons they haunt, and designs the checks to catch them by name.
A misconception is not a gap
A knowledge gap means the concept has not formed yet — the fix is to rebuild it. A misconception is different: a confident, specific wrong model that produces wrong answers systematically. Repeating the lesson does not dislodge it, because the learner does not think they are missing anything. It has to be surfaced and confronted directly.
That only works if the system knows which misconception is present. So Maestro treats misconceptions as first-class, named objects: every course carries a registry of canonical misconceptions, each with a statement, severity, the diagnostic distractor that detects it, the error pattern it produces, and a confirming probe.
The pipeline: generated, criticised, gated
- The Registry Generator drafts. From the course's knowledge components and the “tempting errors” the Lead Designer identifies as it designs each lesson, an AI generator seeds canonical entries in a shared registry — every one marked pending.
- The Misconception Critic tests. Each entry is checked against four bars — Specific, Plausible, Distinct, Detectable. Near-duplicates are flagged for merging; missing misconceptions are flagged with justification. The Critic prepares; it approves nothing.
- The human gate decides. A faculty reviewer approves, edits, or rejects every entry and every proposed lesson binding. Nothing binds without passing the gate, and an approved entry that is later edited returns to pending. Provenance — who, when, in which review mode — is never ambiguous.
- Bindings wire the traps. Approved misconceptions are bound to the lesson whose check scores against them. That check then carries traps wired from those bindings, and its diagnosis can name the exact misconception an answer confirms.
The guardrail
Some lessons exist specifically to separate confusable ideas or repair known wrong beliefs. For those, a check without misconception bindings would be a diagnostic pretending to be one. And because every lesson must carry its mix-up message and its other remediation pieces before it can publish, a named misconception always has somewhere to send the learner.
The payoff is precision: the same wrong answer that a generic quiz would mark “incorrect” is diagnosed as this misconception. The learner sees a plain “A mix-up”, never the internal label, and is offered content that confronts that specific wrong model — not a repeat of the lesson they already sat through.
Why a registry, not ad-hoc traps
Because misconceptions recur — across items, lessons, and cohorts. A canonical, per-course registry means the same wrong model is named consistently everywhere it appears, evidence about it accumulates coherently in the learner model, and faculty review it once instead of rediscovering it in every item.