Physical AI Ethics Audit

3.05

Derivation Chain

Step 1 Spread of physical AI in classrooms

→

Step 2 Mandatory AI ethics education

→

Step 3 Ethics audit service for educational robots

Problem

Elementary, middle, and high schools and education offices introducing educational physical AI robots must comply with AI ethics guidelines (Ministry of Education AI ethics standards, student data protection), but lack the capability to self-inspect the scope of processing of student voice, face, and behavior data collected by the robots. External audits cost over 10 million KRW (approx. $7,500) per case, and there is no universal checklist because standards vary by education office.

Solution

A SaaS that automatically checks against Ministry of Education AI ethics standards, the Personal Information Protection Act, and the Child Protection Act when users input the data collection items and processing methods of the introduced robots, and generates corrective action guides for each non-compliance item. It also provides automatic parental consent form generation and education office report templates.

Target: Information department teachers at elementary, middle, and high schools introducing educational AI robots, and AI education supervisors at provincial and city education offices.

Revenue Model: Per school annual license: 490,000 KRW (approx. $367.50), education office group license (10+ schools) per school 350,000 KRW (approx. $262.50), additional audit report generation per transaction 50,000 KRW (approx. $37.50).

Ecosystem Role: Regulation

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

5.0/5

U Urgency

2.0/5

M Market

2.0/5

R Realizability

4.0/5

V Validation

2.0/5

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.

N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (78%)

Tech Complexity

34.7/40

Data Availability

23.3/25

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 (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	/ 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

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

Dashboard