B

University AI Curriculum Auditor

2.85

Derivation Chain

Step 1 University AI basic curriculum development grant program (20 universities, 300 million KRW each)
→
Step 2 University demand for AI curriculum development and quality verification
→
Step 3 Automated industry-relevance auditing of AI curricula

Problem

The government is funding 20 universities with 300 million KRW (~$225,000) each for AI basic curriculum development, but there is no objective means to verify how well each university's designed curriculum aligns with actual industry AI job requirements. Ministry of Education reviewers take 2–3 days per curriculum review, and cross-referencing with industry job analysis data is done manually, resulting in inconsistent evaluations.

Solution

A SaaS where universities upload AI curriculum documents for automatic comparison against an industry job requirements DB, generating fitness scores, missing competency identification, and improvement recommendations as a Report. Core features: (1) automated curriculum-to-job-competency mapping, (2) missing/excessive course identification, (3) peer university benchmark comparison.

Target: Academic affairs offices at the 20 universities participating in the AI curriculum development grant program, Ministry of Education / National IT Industry Promotion Agency evaluators
Revenue Model: Institutional SaaS at 12 million KRW (~$9,000)/year per university (unlimited audits). Custom Reports for government agencies at 5 million KRW (~$3,750) each.
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
3.0/5
M Market
2.0/5
R Realizability
3.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (68%)

Tech Complexity
29.3/40
Data Availability
18.8/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (51/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
14.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

AI/ML [medium] Backend [medium] Frontend [low]
Dashboard