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Universities selected for the Seoul RISE and Regional RISE programs must report AI education outcomes (enrollment numbers, employment rates, industry-academia partnerships, etc.) to the Ministry of Education quarterly. One to two dedicated staff members spend 40-60 hours per report manually aggregating data by department and program in Excel, with a 30% rate of correction requests due to manual entry errors.
Automatically collects AI education KPIs (enrollment, completion rate, employment rate, industry-academia partnership count) from university academic and employment management systems, and provides a dashboard formatted to Ministry of Education reporting requirements. Generates quarterly reports with one click, including year-over-year performance trend visualizations and anomaly alerts.
| 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. |