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As education offices push AI education transformation, teacher AI training has become effectively mandatory. However, training records are scattered across multiple providers (KERIS, provincial education training centers, private vendors), forcing school administrators to spend 5–8 hours per month tracking each teacher's AI competency status. Education office–level AI training completion aggregation takes 2–3 weeks, creating bottlenecks for policy planning.
Automatically collects and consolidates teacher AI training records via cross-referencing across training providers, presented in a unified dashboard. Includes school-level and subject-level AI competency metrics + automatic alerts for teachers with incomplete training + one-click aggregate reports for education offices.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |