B

Local University Department Transition Curriculum Design

2.85

Derivation Chain

Step 1 Local university restructuring and Dong-eui University personnel changes
→
Step 2 Designing new departments after mergers and closures
→
Step 3 Automation tool for new department curriculum design

Problem

When local private universities merge or create departments amid enrollment reductions, curriculum design takes 3-5 professors over 6 months or more. Designing curricula disconnected from industry demand leads to lower employment rates, creating a vicious cycle of repeated disadvantages in Ministry of Education evaluations. Especially when creating interdisciplinary departments like AI and data science, the lack of expertise among existing faculty results in low curriculum quality.

Solution

Based on industry demand data (job posting keyword analysis, NCS job competency maps) and benchmarking of similar departments at other universities, automatically generates a draft semester-by-semester curriculum (required/elective courses, credit allocation, practical training weight) when department goals are entered. Includes optimization of faculty expertise and course matching.

Target: Academic affairs offices and department creation task forces at local private universities (enrollment 3,000-10,000 students, approximately 100 institutions nationwide)
Revenue Model: Project-based: 5 million KRW per project (approx. $3,750) (curriculum design for one department). SaaS annual subscription: 2.4 million KRW per university (approx. $1,800) (benchmark data + course database access).
Ecosystem Role: Education
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 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 (70%)

Tech Complexity
29.3/40
Data Availability
20.4/25
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 (56/100)

Competition
8.0/20
Market Demand
9.4/20
Timing
16.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
10.5/15
Solo Buildability
5.0/10
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] Backend [medium] Frontend [low]
Dashboard