The Effect of Use of AI.

Disruptors

Akshat Mehta (O.P Jindal Global University) Manmohan Hooda (O.P Jindal Global University) N. Prasanth (O.P Jindal Global University) Daksh Dahiya (Maharshi Dayanand University)

Daily life / student life / campus

Our team would like to work on the trade-off between short term academic productivity versus long term cognitive development. It is an everydaypart of our life as students in that many of us have become extremely dependent on AI for assignments and in preparing for examinations so much so that simple tasks of preliminary research and structuring of an argument or an argumentative paper is being delegated to AI. This results in losing the ability to think critically.

AI is not equally accessible. The quality of AI a student can use is directly determined by how much they can pay and this gap is not closing, but rather it is widening. Free tiers are shrinking. Premium subscriptions are climbing. Some users already spend the equivalent of ₹50,000 a month on AI, effectively gaining a personal expert worker that no unprivileged student can compete with. Unlike Google, which gave everyone the same search engine regardless of income, AI is tiered by design. A student who cannot afford a premium plan is not getting a lesser version of the same thing, they are accessing a completely different tool. This creates a educational disadvantage that follows students into their careers. The AI market itself makes this more urgent. Whether the current bubble bursts due to a breakthrough disruption, stabilizes as companies find sustainable margins, or intensifies as running costs push prices higher. In none of these futures does the student without financial means benefit. A solution built on commercial subscriptions is not a solution at all. It is a dependency on pricing decisions made by foreign corporations.

We are students who have watched this inequality play out in our own classrooms. Our goal is not to describe the problem but to build the infrastructure that makes it irrelevant. We want to demonstrate that premium-quality, multilingual AI access for every student regardless of institution size, income, or language is achievable today, using tools that already exist, at a cost that any school or government can sustain. Our learning objective is to design, validate, and document a deployment model that outlasts any single company's pricing decision.

Most proposals to democratize AI access rely on the goodwill of corporations or the budgets of governments. Ours does not. We propose an open-source, institutionally-deployed AI infrastructure model built entirely on free, openly-licensed components — models, serving software, interfaces, and translation tools — that any school or university can own and operate independently. The core insight is simple: treating AI like electricity rather than a subscription. Shared regional infrastructure, funded once as a capital investment the way schools fund libraries or laboratories, serves every student without recurring per-user costs, without data leaving the institution, and without exposure to any external pricing change. The system is designed with a multilingual translation layer covering 200+ languages, meaning a student studying in Tamil, Bengali, or Urdu receives the same quality of assistance as an English-medium peer. It handles peak load through intelligent queuing rather than hard cutoffs, so students are never blocked at the moments they need access most. And it scales from a single school to a district-wide grid as institutional capacity grows. What separates this from other democratization proposals is that we are not asking anyone to wait for policy, funding, or corporate generosity. The architecture works today, the components exist today, and the economics are already better than any subscription model at meaningful scale. We are building the deployment playbook — the thing that turns available technology into accessible infrastructure.