B

AI Ethics Education Case Bank

2.80

Derivation Chain

Step 1 AI fear Report + university AI trainer education transformation
→
Step 2 AI ethics/safety Education content demand
→
Step 3 AI ethics case DB that educators can use directly in class

Problem

University professors and corporate AI Education coordinators must manually collect and organize real-world cases relevant to the Korean context (bias, privacy violations, deepfake harm, etc.) every time they design AI ethics courses or training. While English-language cases are plentiful, adapting each one to Korean legal and cultural contexts takes 2-3 hours per case.

Solution

Automatically collects domestic and international AI ethics issue cases, structures them within the Korean legal framework (Personal Information Protection Act, AI Basic Act, etc.), and auto-generates 'ready-to-teach kits' (case summaries, discussion questions, quizzes, slide templates). Supports filtering by topic and difficulty level.

Target: University AI/Data Science professors, corporate AI ethics Education coordinators, ages 35-55
Revenue Model: SaaS ~$22/month per educator (5 kit generations/month), institutional license ~$1,500/year (unlimited, 10 accounts)
Ecosystem Role: Education
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
2.0/5
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.
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
22.5/25
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
7.5/15
Solo Buildability
8.0/10
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

Data Pipeline [medium] AI/ML [medium] Frontend [low]
Dashboard