AI That Does College For You Got Shut Down. Why It Won’t Matter

Last week, a product appeared that has shaken higher education. Einstein AI is built by Advait Paliwal, a Michigan State University 2024 graduate, and it has been marketed as a total digital proxy for students.

What that means is it logs into Canvas, which is used by half of North American colleges and colleges around the world. The artificial intelligence agent can then watch lectures on behalf of students. It reads essays and it writes papers automatically. It also posts in discussion boards with contextual replies and submits assignments before deadlines without any student input. It was billed as the AI that does college for you.

The site was taken down last week by a cease and desist. The takedown was for trademark infringement, not its actual capability. As you can imagine, the platform went viral and the educator response was panicked, adversarial, and reactive.

The Technology Behind Einstein AI

The tech behind Einstein AI was nothing new if you've been following AI developments. It is part of a wave of new agentic capabilities that emerged at the end of 2025 and early 2026. These include open source platforms like OpenClaw, which took the tech world by storm in January. In fact, the creator of Einstein AI, Paliwal, called it OpenClaw as a student.

OpenClaw is an open-source agentic AI framework and was one of the fastest-growing GitHub repositories in history. The agent receives a goal, navigates its environment, and then executes steps. It makes small decisions on its own to complete the user’s request and can use computer software, platforms, and browsers to get the job done.

Right now, the technical barrier is modest. But it is dropping fast. Within a year, we will likely see something like this as a basic browser extension.

So why does shutting Einstein down change nothing about the new landscape? The commercial product might be gone, but the open source capabilities remain permanent. Anybody with a small amount of technical ability will be able to create something like this for their own personal use and possibly for commercial use in the future.

The next version of a technology like this will not announce itself with a marketing campaign like Einstein AI did. It will simply appear quietly for students to adopt on a website like GitHub.

The Limits of Lockdown

So what can educators do about this, and should they do anything?

We have already seen certain universities over the last few years respond to AI by locking down their browsers, assuming that students are cheating using their physical devices. However, these agents can run on remote cloud virtual machines instead. The student’s device does not even need to be on for an AI agent to complete tasks like this.

A cloud-to-cloud connection looks like normal Canvas traffic. Instructure, the company behind Canvas, acknowledges that server-side detection is nearly impossible. Determining whether it is an AI agent or a human accessing the Canvas learning system is not likely to be an option going forward. Agents would navigate Canvas exactly like a human would.

This is part of a broader shift. It is becoming trivial for students to outsource their schoolwork to AI. Einstein is just the latest step.

This is problematic for education. Many college systems use a learning platform like Canvas to deliver content, then the student produces a static deliverable such as an essay. After a quick quality check, the student moves on to the next stage of the factory line. It’s been a convenient way to move students through a pipeline of education for decades now. AI is exposing the inherent flaws in this mass market process. In many cases, learning has become a faceless product rather than an intimate human process.

When tasked with producing learning products, schoolwork can become a hurdle to overcome, rather than a journey to undertake. I wrote about this problem with schoolwork last year in an interview with a high school student from San Diego. In that article, I argued that the only way forward would be to make student tasks more meaningful so that students were not likely to outsource their work.

The only real path forward is a return to interactive, human learning. A technological arms race from educational institutions is a fantasy and would use a lot of resources for not much benefit.

The Skills Gap

Educators entrenched in the system and lacking AI awareness have unsurprisingly been caught off guard. A Coursera report in 2026 states that only 25 percent of educators are confident using AI. Only 20 percent have AI literacy formally in their curriculum.

In 2025, the National Literacy Trust reported that 67 percent of teachers want more AI training, 81 percent lack time for training development, 75 percent lack knowledge for AI curriculum, and 56 percent of students and educators say higher education is unprepared.

Educators who do not understand agents cannot design against them. They cannot advise students and cannot advocate meaningful policy.

This means we have a coming together of a huge problem in education. Advanced capabilities of this technology are accelerating, while awareness, skills, and time from an educator’s point of view are lacking.

Shifting From Product to Process

So what can education do?

There needs to be a shift from product to process. A shift from evaluating deliverables to evaluating the cognitive process. Tools like Einstein AI can deliver outputs. But they cannot prove the cognitive process of the student, because there is no cognitive process of the student.

How do we assess that?

Does it come from oral defences, where students explain and defend their work verbally? Socratic questioning of a student? Maybe process-based evaluation like peer reviews, reflections, or version histories that anchor assessment in lived experience. Video logs could preserve the embodied reality of thinking. Class debates that demand spontaneous cognition. Collaborative problem solving that cannot be faked by agents.

Good teachers already do this. For pressured teachers, it is more difficult. But tools like Einstein AI and the increasing use of agentic AI mean that being able to assess the process of learning is now mandatory.

Assessing only the deliverables of education is probably no longer worth it unless the student is completing an assessment within the view of an educator. That is not practical in many scenarios, but it may be necessary for some courses.

Policy Is Not Enough

From a broader perspective, we are starting to see policy and institutional responses. Certain states are beginning to require school AI policies legislatively and the United States Department of Labor has published an AI literacy framework for 2026. AI skills are being recognized as foundational professional skills across sectors.

But policy without understanding, policy without practice, policy without reflection is just a piece of paper. It is just a PDF on a website. A bigger change needs to come.

Einstein will not be the last tool of its kind. The next one will appear quietly on GitHub or as a commercially packaged app. Students will likely understand the technology better than their educators.

The next time this happens, will education be shocked again, or will it be prepared? Prepared in a way that makes assessment and college work more meaningful rather than defaulting to lockdown.

The survival of the education system depends on educators. It does not depend on better blocking technology. Our purpose must come before technology. The purpose of education must come before technology, always.