University AI Coursework Plagiarism Policy Builder

4.50

Derivation Chain

Step 1 AI Framework Act implementation and university standards development

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Step 2 University AI usage education policy formulation

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Step 3 Custom AI usage assessment criteria generator for professors

Problem

With the AI Framework Act in effect and universities establishing AI standards, professors must define AI usage boundaries and plagiarism criteria for each individual course, but no clear guidelines exist. A single professor spends an average of 8 hours per course creating AI usage policies for 3-5 courses, and inconsistent standards across professors within the same department cause student confusion and an average of 15 formal complaints per semester.

Solution

Select department, course type (theory/lab/project), and assessment method (exam/assignment/portfolio) to auto-generate a course-specific AI policy document covering permitted AI usage scope, citation formats, and plagiarism criteria. The tool verifies policy consistency at the department level and also generates a student-facing guide PDF.

Target: University professors and adjunct lecturers, Center for Teaching and Learning (CTL) administrators

Revenue Model: Free for individual professors (up to 2 courses), Premium at ~$7.50/month (unlimited courses + department consistency verification), University license at ~$1,350/year (campus-wide professor access + CTL admin dashboard)

Ecosystem Role: Education

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

4.0/5

U Urgency

5.0/5

M Market

4.0/5

R Realizability

5.0/5

V Validation

4.0/5

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 (77%)

Tech Complexity

34.7/40

Data Availability

22.5/25

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 (60/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

18.0/20

Revenue Signals

9.0/15

Pick-Axe Fit

10.5/15

Solo Buildability

8.0/10

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