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Documentation Index

Fetch the complete documentation index at: https://cors-lau.vercel.app/docs/llms.txt

Use this file to discover all available pages before exploring further.

CORS (Course Offering Recommendation System) is an AI-powered academic planning platform built for university administrators. It analyzes historical enrollment data and student migration patterns to recommend which courses to offer each semester, how many sections to open, and how to schedule them — all from a single dashboard.

Quick Start

Get up and running with CORS in minutes. Upload your data and run your first recommendation.

Recommendation Engine

Learn how the ML engine scores courses and generates semester offering recommendations.

Data Management

Upload course catalogs, historical offerings, and faculty records to power your analysis.

API Reference

Integrate CORS into your workflows using the REST API.

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What CORS does

CORS solves a core challenge for academic planners: deciding which courses to offer next semester, and how many sections each course needs. Instead of relying on intuition or manual spreadsheet analysis, CORS uses machine learning models trained on your institution’s historical enrollment data to surface high-confidence recommendations.

Course Recommendations

AI-scored recommendations with latent demand, bottleneck analysis, and projected section counts.

Prerequisite Graph

Interactive visualization of course dependencies and systemic bottlenecks.

Timetable Scheduler

Drag-and-drop scheduling with automatic conflict detection for rooms and faculty.

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Getting started

1

Sign in

Log in with your institutional credentials. CORS uses two-factor authentication — you’ll receive a verification code by email.
2

Upload your data

Import your course catalog (JSON), historical enrollment records (CSV/XLSX), and faculty roster (JSON/CSV/XLSX) from the Data Management page.
3

Run your first analysis

Navigate to the Recommendation Engine, select your campus and expected enrollment numbers, then click Execute Analysis to generate AI-powered recommendations.
4

Build your schedule

Take recommended courses to the Timetable Scheduler to assign time slots, rooms, and faculty — then export the final schedule as Excel.

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Supported campuses

CORS supports dual-campus operation with independent ML models calibrated for each campus:
  • Beirut — separate ensemble model trained on Beirut enrollment history
  • Byblos — separate ensemble model trained on Byblos enrollment history
Select your campus in the Recommendation Engine before running an analysis to ensure campus-appropriate predictions.