B

Korean Language Curriculum Builder for International Learners

3.45

Derivation Chain

Step 1 Fulbright Korean language program / international student education cooperation MOUs
→
Step 2 Growth of Korean language education market for international learners
→
Step 3 Curriculum design tools for Korean language institutions
→
Step 4 Customized curriculum auto-design SaaS

Problem

Instructors at Korean language education institutions (university language centers, King Sejong Institutes, private academies) manually design curricula tailored to each learner's nationality, native language, and goals (TOPIK prep, daily conversation, business Korean), spending 10-15 hours per month per instructor on updating materials and differentiating across proficiency levels. While standard curricula exist, class composition changes every semester, requiring constant readjustment.

Solution

Takes learner profiles (nationality, native language, current level, learning goals) as input and uses an LLM to auto-generate weekly curricula (textbook unit mapping, supplementary materials, quizzes). Major textbooks like Sejong Korean and Seoul National University's series are structured in a database to ensure curriculum continuity even when switching textbooks, with dynamic adjustment based on learner progress.

Target: Instructors at university language centers, King Sejong Institutes, and private Korean language academies, aged 30-50, with class sizes of 10-20 students
Revenue Model: SaaS flat rate at ~$14.15/month per instructor account, institutional pricing for 5+ accounts at ~$11.15/month per account, 14-day Free Trial
Ecosystem Role: Supplier
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
4.0/5
U Urgency
3.0/5
M Market
3.0/5
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 (73%)

Tech Complexity
29.3/40
Data Availability
23.3/25
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
12.0/20
Revenue Signals
9.0/15
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 [medium] Frontend [low]
Dashboard