EduAiQ: Intelligent Quota Management for Academic AI Access

EduAiQ: Intelligent Quota Management for Academic AI Access

Faculty, staff, and students increasingly rely on AI chatbots like ChatGPT for teaching, research, and day-to-day academic tasks. However, providing this level of access at scale presents a major challenge. Purchasing individual ChatGPT Plus licenses across an entire university would be prohibitively expensive, and would also lock the institution into a single provider—a risky move given how rapidly the AI ecosystem is evolving, with no clear long-term winner.

Further, different academic needs call for different AI models—humanities may prioritize conversational fluency, while STEM disciplines may require code generation and technical reasoning. Meanwhile, a new wave of open-weight models like LLaMA, DeepSeek, and Qwen are rapidly approaching or matching ChatGPT’s capabilities, and can be deployed at a fraction of the cost—especially via serverless inference providers such as Together.ai.

At the same time, educational institutions need centralized tools to manage and allocate AI resources—allowing administrators to set usage policies and quotas across colleges, departments, faculty, courses, and students, while staying within contractual or budgetary limits.

This requires both technical flexibility and governance infrastructure—a solution that respects budgets while still fostering innovation.

The Solution: EduAiQ

EduAiQ could be an open-source platform designed specifically for academic institutions to provide affordable, controlled, and scalable access to AI model APIs. It integrates with GPT4All, a powerful open-source ecosystem that delivers a ChatGPT-style chat interface and supports multiple backend model providers. GPT4All already connects to APIs like ChatGPT and can be extended to work with other closed and open-weight models (e.g., Claude, Gemini, DeepSeek, Mistral) via providers like Together.ai.

With EduAiQ, educational institutes can:

• Deploy a single, unified chat interface like ChatGPT using GPT4All with open-weight Model APIs 
• Avoid paying for per-user licenses by managing backend API usage directly
• Choose and switch between AI models based on performance, price, or policy
• Support both commercial and open-source models to optimize cost and compliance

Hierarchical Quota Management

The platform also includes a robust multi-tiered quota and access control system. Administrators at the university, college, department, faculty, and course levels can:

• Allocate and enforce usage limits
• Inherit or override quotas from higher levels
• Assign separate quotas for teaching vs. research
• Track usage metrics to forecast demand and control costs

This hierarchical structure ensures flexibility, accountability, and autonomy—without breaching budgetary or contractual limits with API providers.

Here's a breakdown of the concept and its key components:

Key Concepts

Quota Hierarchy

• University Admin → manages global contract with model providers and sets quotas for each college.
• College Admin → gets a share from the university quota or buys its own; allocates to departments; sets quotas for various departments.
• Department Admin → gets a share from the college quota or buys its own; sets quotas for faculty (research & teaching) and courses.
• Faculty → gets separate quotas for teaching and research;
• Course → gets a quota that students use for assignments, labs, etc.

Admin level rights:

• Can allocate quotas downwards as percentages or fixed limits.
• Can inherit quota from the level above 
• Can acquire/buy their own (grant-funded, special budgets, etc.).
• Can track and adjust usage in real-time.

Functional Requirements

Quota Management Interface

• Web-based dashboards for each level of admin (University, College, Dept., etc.)
• Set, update, and track quotas
• Allocate quotas as percentages or fixed values
• View usage reports and forecasts

Authentication & Role-Based Access Control

• Integration with university SSO systems (e.g., Shibboleth, CAS)
• Fine-grained permissions based on role (student, faculty, admin)

Usage Metering and Billing

• Track API usage (input/output tokens)
• Map usage to users and roles
• Generate reports and optionally chargebacks (e.g., deduct from grant)

API Gateway or Proxy Layer

• A middleware that routes requests to various model providers
• Enforces quotas and access rules
• Logs usage for accounting

Extensibility

• Support for multiple API providers 
  • Private models (ChatGPT, Claude, Gemini, Grok), 
  • Open-weight inference API providers such as Together.ai for open-weight models such as  (Llama, Deepseek, Mistral, Qwen)
• Pluggable system to add new models/contracts
• Allow export of usage data for integration with university ERP/billing systems

Benefits

• Cost control and accountability at every level.
• Scalability across diverse use cases: teaching, research, student projects.
• Autonomy for departments/faculty with oversight from higher levels.
• Encourages responsible use of expensive API resources.

Possible Tech Stack

• Backend: Node.js, Python (FastAPI), or Go
• Frontend: Next.js and React (with role-based dashboards)
• Database: PostgreSQL for quotas and usage data
• Auth: SAML/OAuth integration with university identity providers
• Logging/Analytics: ELK stack, Kafka, Spark