RISE Program AI Education Performance Dashboard

3.85

Derivation Chain

Step 1 University RISE program (AI department establishment and education innovation)

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Step 2 Performance reporting obligations for RISE-participating universities

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Step 3 AI education KPI auto-collection + performance dashboard

Problem

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.

Solution

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.

Target: Administrative staff at RISE program-selected university project offices, and university education innovation center staff (approximately 40 universities nationwide)

Revenue Model: SaaS Annual Subscription at $4,500/year ($375/month) (600만원/year) per university, initial setup fee of $1,500 (200만원)

Ecosystem Role: Supplier

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

4.0/5

M Market

3.0/5

R Realizability

4.0/5

V Validation

4.0/5

NUMR-V Scoring System

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.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (72%)

Tech Complexity

29.3/40

Data Availability

23.1/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

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.

Market Validation (64/100)

Competition

8.0/20

Market Demand

9.4/20

Timing

18.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

13.5/15

Solo Buildability

5.0/10

Validation Breakdown

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.

Technical Requirements

Data Pipeline [medium] Backend [low] Frontend [medium]

Dashboard