Education Office Chatbot Performance Dashboard

2.85

Derivation Chain

Step 1 Education office AI chatbot adoption

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Step 2 Education office inquiry chatbot operations

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Step 3 Chatbot performance analytics and improvement tool

Problem

Provincial and metropolitan offices of education are deploying and upgrading public inquiry chatbots, but lack systematic tools to measure chatbot response accuracy, issue resolution rates, and user satisfaction — making it impossible to prove whether chatbots are actually effective. Offices like the Busan Metropolitan Office of Education invest hundreds of millions of won in chatbot upgrades while a single staff member manually compiles effectiveness metrics in Excel, taking 1-2 weeks per quarter.

Solution

Automatically collects education office chatbot logs and visualizes response accuracy, unresolved escalation rates (chatbot-to-phone), and resolution time by inquiry type on a real-time dashboard. Provides LLM-based automated response quality scoring, unresolved inquiry type clustering, and auto-generated performance reports for legislative and audit reporting.

Target: IT departments at 17 provincial/metropolitan offices of education and subordinate education support offices (public administration professionals, ages 30-50)

Revenue Model: SaaS monthly subscription at ~$295 (390,000 KRW)/education office; usage-based pricing of ~$0.0004 (0.5 KRW) per log entry beyond 100K entries

Ecosystem Role: Supplier

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 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 (63%)

Tech Complexity

24.0/40

Data Availability

19.4/25

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

9.4/20

Timing

14.0/20

Revenue Signals

7.5/15

Pick-Axe Fit

7.5/15

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 [medium] AI/ML [medium]

Dashboard