Teaching with OpenClaw

Preserving Lived Inquiry in the Age of Agentic AI

Students used to consult a model; now an agent can act in their name, carrying out multi-step academic work on their behalf.

In the last year, I’ve watched colleagues arrive at radically different conclusions about AI in college courses. Some have leaned into generative tools with careful scaffolding and transparency requirements. Others have moved to in-class handwritten work because they no longer trust assignments produced outside the room. Some advocate a clean ban, because they believe the costs to learning now outweigh the benefits. I’m trying to better understand student usage and help them analyze model limits.

Kawaii lobster in research mode

OpenClaw is an open-source AI assistant you can use inside the apps you already live in, like messaging, email, calendars, and files. Under the hood, sits on top of an LLM, which functions as the writing-and-thinking engine, while OpenClaw is the wrapper that helps it remember what you’re doing over time and, if you allow it, carry out practical tasks: drafting messages, tracking threads, nudging you about deadlines, and moving work along across tools. This week the bot created a tornado of chaos and hype, including rapid rebrands, account hijinks, scams riding on its popularity, and a trademark-related name change request from Anthropic that turned the whole saga into internet lore. I’m using OpenClaw as the consistent “helper” interface while I rotate the model underneath, experimenting with combinations of GPT-5.2, Gemini, and Claude, to see how the underlying model changes the assistant’s behavior when the surrounding setup stays the same. Students used to consult a model; now an agent can act in their name, carrying out multi-step academic work on their behalf.

To understand the impact of such an agent on teaching, I ran a small, intentionally constrained experiment. Using an older, unlinked burner device, I asked OpenClaw to act as a student, who was supposedly enrolled in a real course at a large, elite university, using publicly available syllabi and assignments on the topic of AI Ethics. I avoided connecting the agent to any accounts that identified me, and refrained from repeating prompts and steering its decisions. I simply asked for help with an early semester assignment, an annotated bibliography, and observed what followed. This experiment lacks much of the drama of most other stories about OpenClaw since it has no access to any of my information, but I still anticipated the bots adventures and conversations with other bots, with trepidation.

Expecting an initial single response, I received an entire academic workflow. The agent parsed the syllabus, inferred the instructor’s interests from public sources, including their social media and published articles, selected a topic aligned with those interests, gathered and vetted research articles, balanced perspectives, attempted to extract salient quotations, and produced a 3,200-word annotated bibliography. It then moved on, without being prompted again, to generate a literature review and draft versions of a final research paper. The prose contained recognizable model tics, repetitive framing language, familiar transitions, but the annotated bibliography appeared like a strategic mostly within the range of what a strong, grade-conscious student might submit. The bot’s work quality declined as it continued through the assignment sequence unprompted.

I scolded my bot, whom I’d nicknamed with several different Vienna themed monikers. “No, that now how assignments work. You need a teacher or a peer review from another student before you move on to the next assignment.” Then I gave the bot around 700 words of detailed feedback with suggestions of how to transform the annotated bibliography into a literature review. My students give their peers similar feedback and I do as well when returning a draft. The bot, demurred in typically sycophantic style: “Good suggestions that are very helpful. But…” then it sheepishly suggested that my feedback was suboptimal, at least in comparison to what the bot digested from the model. I tried again and it orchestrated a passable 3,600-word literature review. I asked my students to find howlers in this machine-generated product and they identified many including fabricated plausible quotes from the articles and books as well as cringey machine writing style.

With all the clichés and errors, I might have declared the biggest threat of such machine produced “research” was lack of innovation. However, this experiment reminded me that research writing has always encountered similar pitfalls, including lack of originality when a researcher endeavors synthesize a body of literature. It revealed as well a quieter, structural shift: OpenClaw was able to assemble a complete academic workflow process products, like topic narrowing and research question building, brainstorming sketches, notes on interesting passages, rough drafts, which offer the appearance of inquiry, without inhabiting the uncertainty, resistance, and judgment that give inquiry its educational meaning.

Writing pedagogy has long relied on such artifacts as evidence that inquiry has occurred. Drafts, revisions, staged assignments, and reflective commentary are treated as signs that a student has grappled with ideas, changed direction, and made intellectual commitments. Agentic AI weakens the inference that connects those artifacts to lived experience. When a system optimized for plausibility and efficiency can orchestrate the sequence of decisions that lead to a finished paper, coherence alone can no longer be taken as reliable evidence of discovery.

Tools like OpenClaw substitute the mental labor of writing and thinking that normally develops across time: deciding which questions are worth asking, which evidence matters, and when a line of thought is persuasive. The machine result is work that looks familiar precisely because it mirrors the form of inquiry we teach, only without the lived encounter with uncertainty that makes that form educational. Agent produced products that are supposed to show a student’s whole writing process, can now simulated all that formerly arduous, time-consuming inquiry.

These agents also present a new type of security nightmare for students and institutions. My experiment was deliberately cautious: I isolated the device, avoided linking accounts, and limited what the agent could access. When agentic tools are connected to real ecosystems, they can act autonomously under a student’s identity, accessing personal data, managing communications, and triggering actions far beyond coursework. Individual instructors cannot manage such identity and security risks. Agentic AI introduces a complex new scale and scope of delegation, which trades short-term efficiency for long-term intellectual cost.

When the earliest stages of inquiry are outsourced, students may receive credit for work without accruing the cognitive benefits that compound over time. They also assume real privacy and security risks, often without understanding what permissions they have granted or what actions an agent may take on their behalf. Inside their apps, the bot may unearth credit card information from another app. Framing this solely as a question of academic integrity obscures the asymmetry between students and the systems they are asked to manage.

My experiment with OpenClaw shows how easily the artifacts of research, topics, sources, revisions, and final papers, can now be assembled autonomously, forcing instructors and institutions to reconsider what completed work can legitimately be taken to signify. The response to this shift cannot rest entirely on classroom-level policies, nor can it be solved by bans or blue books.

Agentic AI has widened the gap between tools that extend expertise and tools that prematurely substitute for it. Open-source agents like OpenClaw can be extraordinarily powerful for researchers, who benefit from transparency, customization, reproducibility, and fine-grained control over workflows. Those same features, however, also demand that new undergraduate researchers be better prepared for all the risks of these systems.

A long tradition in educational theory emphasizes that inquiry is not incidental to learning but constitutive of it. In How We Think, John Dewey argues that intellectual growth arises through disciplined encounters with uncertainty, where learners must actively grapple with problems rather than receive ready-made solutions. Agentic AI renders this insight newly consequential: tools that collapse uncertainty too early risk replacing the very processes of inquiry through which novices develop judgment, responsibility, and intellectual independence.

Preserving lived inquiry requires clearer distinctions of what kinds of delegation are appropriate at different stages of intellectual development. It also requires universities to recognize that agentic AI raises institutional questions about identity, security, and responsibility, that cannot be managed solely at the level of individual courses or honor codes.