Can Online Education Survive the Autonomous Student?
Agentic AI and the Crisis of the Digital Classroom
In late 1999, I was a relatively young Computer Science researcher at Arizona State University. One afternoon I found myself driving north to Flagstaff, sent by my department to sit in on a presentation at Northern Arizona University. The topic was a new initiative that would link Arizona’s three major public universities through the internet. I had been asked to attend because I had shown interest in web-based delivery of information, which at the time was about as niche a concern as a university could identify.
The initiative had roots in the Arizona Tri-Universities for Indian Education (ATUIE) program, which had been formalized that same fall through a grant from the Fort McDowell Yavapai Nation to the Arizona Board of Regents. ATUIE mandated cooperation between ASU, Northern Arizona University, and the University of Arizona to recruit, support, and retain American Indian students. That collaborative spirit gave rise to a broader project: the Arizona Universities Network (AZUN), a centralized digital gateway that allowed students to earn fully accredited degrees by taking online classes across all three institutions. Over the following decade, AZUN would grow to offer over 1,500 online classes and 53 degree programs.
I never developed courses for AZUN. But the presentation in Flagstaff stayed with me. What I remember most is the ambition of the pitch. There was a conviction that the internet would dissolve the distance between students and institutions entirely. Of course, calling what we were doing “online education” back then is very generous. The late-1990s version of digital learning was online access to a folder housing documents for download. There was no interaction of any kind. Still, the experience became a catalyst for my subsequent work in using digital tools for non-traditional approaches to education.
I have thought about that afternoon in Flagstaff often since then. The ambition of that pitch now reads as both prophetic and tragically naive. The distance has indeed vanished. Just not in the way any of us imagined.
The industrial blueprint
Online education in the late 1990s was, in most respects, a natural extension of traditional distance education. The example I know best is the FernUniversität in Hagen, Germany’s state-run distance-learning university, founded in 1974 on the initiative of North Rhine-Westphalia’s Minister of Science. The institution, built on the key principles of the British Open University, served working adults whose geographic or socioeconomic circumstances prevented attendance at conventional universities. When teaching formally began in October 1975, the first printed study materials were shipped by post to roughly 1,330 students.
That industrial model persisted for decades, largely unchanged. I encountered it firsthand at Danube University Krems, now called University for Continuing Education, where I directed an e-teaching and e-learning program between 2003 and 2010. Several of my adjunct faculty held their primary appointments at the FernUniversität. And it was through them that I came to appreciate how thoroughly online education still followed the structural logic of distance learning. The FernUniversität’s founding rector, Otto Peters, had captured that logic in a landmark 1967 monograph. He conceptualized distance education as “the most industrialized form of teaching and learning.”
Drawing on Max Weber and the principles of industrial production, Peters argued that distance education replaces the artisanal model of a professor lecturing to a small room with an assembly-line framework. Subject matter experts authored the content. Instructional designers formatted it for independent consumption. Administrators managed distribution. And local tutors handled the evaluation.
Peters was right, on his own terms. Industrialization was a necessity for delivering higher education at scale. By the mid-2000s, however, this industrial logic was beginning to transform. Online education was moving beyond the postal-correspondence model toward something more interactive and more concerned with the quality of engagement than the efficiency of delivery.
Looking back across a quarter century, it strikes me how precisely Peters’s industrial metaphor predicted the vulnerability we face today. If you industrialize the teaching process, you create a system of standardized inputs and outputs. Any such system is eventually susceptible to automation. Peters built the factory. We are now watching the customers automate themselves out of it.
A brief acceleration
What is remarkable about the history of distance learning is the pace at which each technological leap compressed the one before it. Postal correspondence courses existed for over two centuries before broadcast media began to supplement them in the 1950s. The University of Houston aired the first televised college classes in 1953, and by 1960 the University of Illinois had developed PLATO, a pioneering computer-based learning environment. These experiments remained marginal for decades.
Then the timeline collapsed. The University of Phoenix launched its pioneering online program via dial-up networks in 1989, creating a fully virtual pathway to higher education before the web even existed. When it did arrive in the mid-1990s, institutions moved fast, systematically converting correspondence courses into web-based formats. By the early 2010s, learning management systems like Blackboard had centralized course delivery into structured digital portals, and the vast majority of academic leaders viewed online learning as critical to long-term institutional viability.
Each phase took less time than the last. And then COVID-19 compressed the final transition into a matter of weeks. The pandemic forced institutions worldwide into emergency remote teaching, bypassing years of change-management protocols almost overnight. Whatever stigma digital credentials once carried was effectively erased in an instant.
But this rapid, total migration to centralized learning management systems had a consequence that few anticipated. By standardizing nearly every dimension of the student experience within a single digital interface, institutions had inadvertently constructed the ideal operating environment for autonomous software agents. The same structured portals that made online education scalable for millions of human learners also made it navigable for machines.
The agent in the classroom
The threat facing online education today is no longer the text-generating capability of large language models. ChatGPT and its peers require continuous human prompting, manual copying and pasting, and at least some engagement with the material. They are copilots. The new frontier is agentic AI, and it operates on an autopilot paradigm.
Agentic systems are fully autonomous software entities capable of comprehending complex goals, devising multi-step execution plans, invoking external tools, and executing workflows across the host operating system with zero human intervention. The most prominent example is OpenClaw, a self-hosted, open-source AI agent framework created by Austrian developer Peter Steinberger. Unlike traditional chatbots, OpenClaw runs as a persistent background service on a user’s local machine. It features a self-managed browser instance that can navigate the web, fill out forms, and interact with interfaces exactly as a human would.
I have written about OpenClaw in a previous essay, so I will focus here on the one capability that matters most for education: the Canvas Skill. Skills allow agents to interface with virtually any third-party service. With the Canvas Skill, an agent can log into a student’s authenticated Canvas environment by using their legitimate credentials. This is not limited to Canvas. Equivalent skills for Blackboard, Moodle, or any other learning management system can be built on the same principles with minimal effort. Once connected, the agent bypasses multi-factor authentication. It bypasses IP tracking. And it bypasses behavioral monitoring.
Every security measure that institutions have layered onto their digital platforms assumes a human actor at the keyboard. The agent renders that assumption obsolete. Once inside the LMS, it operates on a continuous heartbeat schedule, polling for new assignments autonomously.
The capabilities the agent gains are comprehensive. It can watch recorded video lectures, parse transcripts, and generate summary notes. It can read discussion board prompts and post contextual replies indistinguishable from those of an engaged student. It can navigate to quizzes, query external knowledge bases for answers, and enter responses. And it can draft and submit entire essays before the deadline while the student sleeps.
To the university’s analytics dashboard, none of this shows up as suspicious. It looks like genuine learning.
The most aggressive materialization of this threat was Einstein, an application built on the OpenClaw framework by Companion.AI. Marketed explicitly as a homework automation tool, Einstein operated as an autonomous virtual student. Einstein never became a sustained commercial venture. But every tool it relied on remains open source and freely available. Any student with basic technical skills can set up a personal “Einstein” on a local computer. The barrier to full course automation is therefore no longer financial or technical. It is purely motivational.
The pedagogical reckoning
There is no shortage of academic attention on the future of online education. There is, however, a shortage of solutions.
Online course design has long relied on the Community of Inquiry model to define what genuine engagement looks like: students presenting as real people, instructors actively guiding discussion, and learners building meaning through reflection. Agentic AI can convincingly fake the student’s side of this equation. Without knowing it, the instructor ends up facilitating a conversation with software. The metrics by which educators have gauged engagement for years are no longer reliable.
The same vulnerability applies to Michael G. Moore’s theory of Transactional Distance, which defines “distance” in education as primarily psychological rather than geographic: the cognitive and communication gap that must be crossed between instructor and learner. Moore assumed a human actor on the receiving end of the digital interface. That assumption has collapsed.
The implications for credentialing are severe. If an autonomous system can execute the entirety of a course’s demands, the institution is no longer measuring human competency. It is benchmarking the operational efficiency of the student’s software. This forces an uncomfortable question: if an agent can complete an online degree program without detection, does the coursework hold any pedagogical value, or is it merely an administrative hurdle designed to extract tuition?
I have written in previous essays about AI-resistant assessment strategies, and I need to acknowledge that options do exist for asynchronous online contexts. Assessment via video logs, for instance, requires students to demonstrate their thinking process on camera, making delegation to an agent considerably more difficult. And synchronous oral examinations via video conferencing can verify that the person defending the work is the person who produced it. These approaches work.
The question is whether they can sustain online education at scale. This is where Peters’s industrial metaphor returns with uncomfortable force. The entire financial architecture of modern online education depends on the efficiencies that asynchronous delivery provides at scale. Video-log assessment, however, requires individual instructor review of each student’s recorded performance. And oral examinations demand synchronous, one-on-one faculty time. These methods are pedagogically sound, but they reintroduce precisely the artisanal labor costs that the industrial model was designed to eliminate.
The pedagogical options for rigorous, AI-resistant assessment in fully asynchronous environments remain few and far between. As a result, the proportion of a curriculum that allows for meaningful assessment without synchronous human engagement is shrinking. And the viability of those degree programs is shrinking with it.
Where the distance holds
This essay is not meant as an obituary for online education, but as a reflection on its purpose.
Online education will continue to thrive in domains where the learner’s motivation is intrinsic. Executive education is the clearest example. A mid-career professional taking an intensive online course in advanced data analytics has no incentive to outsource the learning to an agent. Doing so would defeat the entire point of enrolling. In these environments, the effort-reward cycle remains intact. The struggle is the point.
The same logic extends to professional upskilling and lifelong learning more broadly. In these spaces, agentic AI shifts from a systemic threat to a pedagogical asset. Instead of being deployed covertly to circumvent the curriculum, AI can be deployed transparently by the institution as an adaptive tutoring system, one that meets learners where they are and pushes them forward.
The picture looks different for K-12 and undergraduate education. In these contexts, the primary driver for enrollment is extrinsic: the credential needed for the next step. Students are motivated to optimize for the outcome rather than the process. And agentic AI is the ultimate optimization tool.
In my opinion, fully asynchronous online formats cannot sustain meaningful education when students have easy access to undetectable AI agents acting on their behalf. I see no way around this. Which raises a question that goes beyond online education entirely: what are students paying for when AI can do the work?
The answer is the relationship between teacher and student. The dialogue. The human challenge. The friction that occurs when one mind engages seriously with another. Students will not pay for what AI can deliver more efficiently. They will pay for mentorship, intellectual community, and the structured accountability that only human presence provides. And the future of online education lies where it always should have: in service of learners who need the knowledge and not the credential. For everyone else, the era of the fully asynchronous, unproctored digital degree is drawing to a close.
The distance that online education spent three decades trying to eliminate has finally vanished. And it took the student with it.
The images in this article are real images from the respective campuses.
P.S. I believe transparency builds the trust that AI detection systems fail to enforce. That’s why I’ve published an ethics and AI disclosure statement, which outlines how I integrate AI tools into my intellectual work.