B

AI Data Labor Curriculum Design

3.30

Derivation Chain

Step 1 Expansion of human labor to power AI
→
Step 2 Increase in demand for training AI data workers
→
Step 3 Curriculum design tools for AI data jobs in educational institutions

Problem

Government-funded vocational training institutions (1 director + 3-5 instructors) designing new job training courses such as AI data labeling and RLHF in accordance with NCS (National Competency Standards) take 2-3 months per course for NCS competency unit mapping, lesson plan creation, and assessment tool development. When HRD-Net registration requirements change, the burden of redesign is significant.

Solution

When a desired job (AI data labeling, prompt engineering, etc.) is entered, it automatically generates NCS competency unit mapping, weekly lesson plan drafts, and assessment tool templates. It also provides a one-stop solution including an HRD-Net registration requirements checklist and draft submission documents for course approval.

Target: Directors of government-funded vocational training institutions and curriculum development staff, small training institutions with 50 or fewer employees
Revenue Model: Per course design: 150,000 KRW (approx. $113), monthly subscription: 59,000 KRW (approx. $44) per month (includes 2 courses per month), annual subscription: 590,000 KRW (approx. $443) (unlimited).
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
2.0/5
R Realizability
4.0/5
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.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (73%)

Tech Complexity
34.7/40
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 (53/100)

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
7.0/10
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

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