Self-Correcting AI Teaching Activity Generator

THE PROBLEM Teachers everywhere — from well-resourced schools to under-resourced classrooms — spend enormous unpaid time designing engaging, age-appropriate lesson activities. AI tools can generate lesson content quickly, but a single-pass AI output can quietly contain real defects: unsourced statistics, rubrics too vague to grade consistently, sensitive topics flattened into simplistic binaries, or timelines that don't fit the class period. A teacher using an AI generator has no way to know which of these problems, if any, made it into their activity. THE SOLUTION This project is a Node.js/Express web app where a teacher enters a subject, topic, optional cross-curricular link, and classroom context (grade level, available resources, class size). The app calls IBM Bob 2.0 as the generation engine to produce a complete, ready-to-teach activity: a narrative hook, timeline, materials list, step-by-step instructions, reflection questions, an assessment rubric, and customization zones — rendered as a clean, printable page with an optional Mermaid flowchart view. What makes this different from a single-shot AI generator is a second, automated self-critique pass. After Bob generates an activity, the app calls Bob a second time to review its own output against six concrete quality checks we identified through a manual audit of an initial batch of test activities: unsourced or overconfident data claims, false binaries that flatten real-world nuance (especially in historical or social topics), unrealistic scope for the stated class time, rubrics that lack concrete observable examples, missing opt-out options for students uncomfortable with public speaking, and internal contradictions between different sections of the same activity. If a real issue is found, the activity is revised at the exact location of the defect — not just patched with a generic disclaimer. If the critique step fails for any reason, the app safely falls back to the original, successfully-generated activity, so a technical hiccup in the quality-check layer never blocks a teacher from getting their lesson. The app also adapts its material recommendations to the classroom's actual resources: a well-resourced school might get a recommendation for lab equipment or a robotics kit, while a low-resource classroom gets the same concept taught with straws and tape, cut paper, mud, or found containers — the same pedagogy, adapted to what's actually available. TARGET USERS Teachers in any subject and any resource context — from fully-equipped STEM labs to classrooms with no budget for materials — who want a ready-to-teach activity they can trust was checked for common AI-generation pitfalls before it reaches their students. WHY THIS IS EFFECTIVE We didn't just build this and assume it worked — we tested it. We manually audited 10 initial test activities against six pedagogical quality dimensions, found a real bug in our own quality-validator script (it was counting punctuation marks instead of actual questions), fixed it, then built the self-critique mechanism and ran it through five additional adversarial test scenarios spanning physics, sensitive history, a contested policy debate, a mock trial, and a science lab — refining the mechanism each time a real gap was found, including catching and permanently blocking a case where the AI fabricated a fake academic citation to satisfy a "cite your source" instruction. Only after that iterative testing did we wire the mechanism into the live app, then verified it end-to-end through the actual running application.