B

Student AI Assignment Portfolio Builder

3.65

Derivation Chain

Step 1 Proliferation of Student-Participatory AI Education Content
→
Step 2 Demand for AI Education Outcome Measurement
→
Step 3 Per-student AI-assisted assignment portfolio builder

Problem

Although AI education was formally introduced in K-12 schools starting in 2025, there is no way to verify the authenticity of student assignments created with AI tools or to track learning growth over time. Teachers spend an average of 15-20 minutes per assignment trying to determine AI usage, while parents cannot objectively assess their children's AI literacy levels. There is also no standardized portfolio format to demonstrate AI literacy for college admissions or job applications.

Solution

The platform automatically records the entire process of students completing assignments with AI tools (prompt inputs, revision history, final deliverables) to generate 'process-focused' portfolios. Core features: (1) automatic AI tool usage history capture (browser extension), (2) automated prompt engineering competency scoring, (3) portfolio export in PDF/link format.

Target: Middle and high school teachers implementing AI education (IT/technology subjects) and tutoring academies/EdTech companies
Revenue Model: School license at $37.50/month per class (30 students), individual Premium at $3.65/month per student, academy license at $112.50/month per branch
Ecosystem Role: Infrastructure
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
3.0/5
U Urgency
4.0/5
M Market
4.0/5
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 (74%)

Tech Complexity
29.3/40
Data Availability
24.4/25
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 (53/100)

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
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

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