Reality-Graded Question QA for ACA Tuition Providers

A private quality-assurance system for independent ACA exam trainers and small tuition firms that use AI to draft mock questions, worked answers, and marking guidance. Instead of trying to infer whether materials improve retention from noisy student outcomes, the product grades each asset against objective, inspectable realities: syllabus mapping, numerical correctness, answer-key consistency, mark-scheme alignment, ambiguity, answer leakage, unsupported explanations, and false confidence. AE fits because it can run adversarial multi-model debate over each draft, enforce structured behavioral contracts for what a valid ACA question or explanation must contain, and then score outputs against deterministic checks plus human-verifiable grading rubrics within hours. The deliverable is not a platform students use; it is a back-office content gate that flags unsafe or low-quality AI-generated materials before release. Buyers pay to reduce reputational risk, tutor review time, and the chance of distributing flawed mocks or explanations that confuse students or mis-teach exam technique.

A private, reality-graded QA gate for AI-generated ACA tuition materials is defensible because it solves a concrete reputational-risk problem with auditable checks and fast ROI, in a market increasingly pressured to prove quality and value.

Engine verdict: GATHER_MORE_SIGNAL (WORTH_SKIMMING). Credible QA wedge, but prove recurring paid pain in ACA providers before building past a manual audit.