Build 'AlgorithmicAudit EDU'—a certified, black-box testing suite for educational software that mathematically proves the absence of engagement-optimizing loops. The system uses adversarial AI agents to interact with a target platform thousands of times, mapping the decision tree to detect if content sequencing correlates with dopamine-triggering metrics (time-on-page, click-through) rather than pedagogical outcomes. It generates a 'Child-Safe Algorithm Certificate' required for school district procurement, ensuring compliance with federal and state minor-protection laws.

Educational institutions and youth-focused platforms are facing a regulatory cliff. Existing social media and content delivery systems rely on engagement-driven algorithms that are now legally prohibited for users under 16 in proposed US legislation. Schools cannot easily audit or verify if third-party educational tools are secretly using engagement optimization (addictive loops) disguised as 'personalization.' The friction lies in the lack of a verifiable, technical standard to prove an algorithm is 'safe' and non-manipulative, creating liability for schools adopting new EdTech tools.