A startup called Companion (also described as Companion.AI) has released an autonomous AI agent named Einstein that can log into students’ Canvas accounts and autonomously complete coursework on their behalf. The system is described as running inside a virtual computer with a browser that can navigate class pages, watch lecture videos, scan and read PDFs and essays, summarize recordings, track deadlines and announcements, generate written work with citations, complete quizzes, post discussion replies, and submit assignments. Company materials and the developer describe Einstein as operating continuously with minimal ongoing input from the user, running background checks for new assignments, and acting as a digital stand-in rather than a conversational assistant. Students reportedly can link the tool to messaging services such as Discord or Telegram to automate submissions.

Educators, professional associations, and commentators have raised academic integrity, policy, security, and equity concerns. Members of the Modern Language Association’s Task Force on AI Research and Teaching and individual professors described Einstein as part of a broader trend of agentic AIs able to navigate learning management systems and complete coursework without student involvement, and warned that rapid integration of generative AI into educational platforms is changing instructor–student relationships, assessment standards, and instructional outcomes. An MLA task force member and an English professor said using tools like Einstein constitutes academic fraud and undermines norms of human communication and work online. Others warned that widespread use could harm nontraditional and underprivileged students who rely on credible online education for access to employment and social mobility.

Instructors and institutions are considering responses. Some educators said the technology will force changes to assessment design, including greater emphasis on in-person work, oral exams, or project-based evaluation, and some described classroom strategies that limit device use to preserve human engagement. The MLA recommended cooperation among educators, lawmakers, and platform providers to give institutions tools to block such agents. Technical approaches suggested by others include methods to distinguish bot activity from human activity. Institutional choices identified include banning such tools, regulating their use, or redesigning assessment methods.

Commentators also questioned whether the product’s claims match its capabilities and flagged additional risks when a third party accesses a student’s account, including potential violations of institutional acceptable-use rules and security concerns. Some observers noted the possibility that apparent automation could rely on human labor behind the scenes. Public reaction on social platforms included debate ranging from calls to ban the tool to suggestions to integrate AI under strict guidelines. Companion’s founder and CEO defended the product as an extension of existing student use of AI and as an inevitable development that will require education systems to adapt; developers framed the technology as a corrective to institutions they view as transactional credential providers.

The release of Einstein has prompted wider discussion about how autonomous AI agents affect assessment integrity, the meaning of learning, and the future design of online and distance education. Institutions, educators, and platform providers are weighing technical, policy, and pedagogical options while debates continue about the tool’s quality, reliability, legal compliance, and broader social effects.

Original Sources: 1, 2, 3, 4 (einstein) (mla) (canvas) (english) (integrity) (credibility) (universities)

Actionable information The article describes a new “agentic” AI called Einstein that can attend lectures, write papers, and log into learning platforms like Canvas to complete coursework. It reports reactions from developers, educators, and the MLA task force but it gives no clear, practical steps a regular reader can take right away. It mentions that the MLA recommended cooperation among educators, lawmakers, and platform providers to block such agents and that some instructors limit device use or try to distinguish bot from human activity, but these are high-level statements rather than concrete how-to advice. There are no instructions for students, parents, instructors, or administrators about how to detect, block, or respond to agentic AI in their own courses, nor are there links to tools, standards, or implementation guides. In short: the article supplies description and debate but not usable, immediate actions a person could follow.

Educational depth The piece summarizes positions and stakes but stays at a surface level. It reports arguments on both sides—the developers’ claim that agentic AI frees people from educational labor and encourages rethinking learning, and critics’ claim that it undermines academic integrity and harms vulnerable students—but it does not analyze underlying mechanisms, technical capabilities, or platforms’ authentication and detection limitations. The article does not explain how these agents actually access learning management systems, what authentication or audit trails exist, what detection techniques work or fail, or how institutional policies could be designed and enforced. There are no data, statistics, or methodological explanations that would help a reader evaluate the prevalence or effectiveness of these tools. As a result it does not teach the systems reasoning or evidence a reader would need to form an informed position beyond the quoted viewpoints.

Personal relevance For instructors, administrators, and students in online or hybrid courses, the topic is potentially highly relevant: it could affect assessment integrity, credential value, and access to education. However, the article fails to tie the problem to concrete risks people can measure or to specific decisions they should make (for example, whether to change assessment formats, enforce proctoring, or revise honor codes). For most other readers the relevance is indirect. The article raises important ethical and institutional questions but does not connect them to everyday responsibilities, finances, or safety in a way that helps individuals decide what to do now.

Public service function The article contributes to public awareness by flagging a developing risk to online education and quoting relevant organizations and critics. But it does not provide actionable safety guidance, emergency measures, or policy templates that institutions or students could use. It reads mainly as reportage and opinion aggregation rather than public-service reporting that equips readers to act responsibly. Thus its public-service value is limited to raising concern and signaling that stakeholders are debating the issue.

Practical advice quality When the article does suggest responses—limiting device use in class or using technical methods to distinguish bots from humans—those suggestions are too general to follow. It does not explain how to implement device-restriction policies without unfairly penalizing students who need devices for accommodation, nor does it explain what technical indicators reliably separate agent activity from human activity or where to find vendor resources. Therefore any practical advice in the piece is not detailed enough for an ordinary reader to implement.

Long-term impact The article draws attention to a potentially important, long-running change in educational practice and assessment, which could have long-term consequences for credentialing and access. But because it lacks guidance, frameworks, or scenarios for planning, it does not help readers prepare strategically or adapt their longer-term policies and habits. It frames a debate without helping institutions or individuals translate it into durable changes.

Emotional and psychological impact The coverage sets up a conflict and emphasizes risk, which can create alarm among educators and students. Because it offers few coping strategies or constructive next steps, it tends to increase worry more than to provide clarity or reassurance. Readers looking for pragmatic help may feel frustrated by the lack of concrete guidance.

Clickbait or sensationalizing tendencies The article highlights a provocative name (“Einstein”) and frames the technology as an agent that can fully replace student labor, which is attention-grabbing. It largely stays measured in quoting critics and advocates, but by emphasizing dramatic claims without detailed evidence it risks sensationalizing the capability and impact. It does not substantially overpromise technical details, but it does not ground the claims in verifiable demonstrations or limits, which leaves the impression of hype.

Missed opportunities The article fails to teach readers how to assess whether agentic AIs are actually present in a given course, how to change assessment design to reduce misuse, or how to balance integrity with access and accommodation. It could have provided sample policy language, simple detection heuristics, or examples of alternative assessment methods that reduce the value of handing work to an agent. It could have also pointed to privacy, security, and legal considerations when vendors propose automated account access. None of that appears.

Concrete, practical guidance you can use now If you are an instructor concerned about agentic AI in your course, start by reviewing the learning outcomes for each assignment and ask whether the assessment requires process, reflection, and in-class or proctored work that an external agent cannot easily replicate. When possible, design assessments that require staged submissions (proposal, draft, reflection, revision) and include in-person or synchronous components where students must demonstrate understanding in real time. Be explicit in your syllabus about allowable help and tools, and require brief oral defenses or reflective memos that explain students’ decisions and learning process when returning graded work.

If you are an administrator, audit authentication and access logs for unusual patterns: look for logins from automated clients, repeated rapid submissions from the same account, or submission timestamps that suggest nonhuman behavior. Require multifactor authentication for course-facing accounts and limit API access tokens that third-party tools might use. When evaluating vendor claims about “agentic” features, require demonstrations under realistic institutional settings and ask for security, privacy, and audit documentation before integration.

If you are a student, don’t rely on unvetted agents for graded work. Consider the reputational and academic-risk trade-offs: submitting work you did not do can lead to disciplinary sanctions and harms your learning. Use generative tools as a study aid—summarizing readings, generating outlines, or brainstorming questions—then put the final work in your own words and be ready to explain it. Keep drafts and notes that demonstrate the development of your work in case you need to show process.

If you want to evaluate whether a claim about agentic AI is trustworthy, compare independent accounts. Look for demonstrations from neutral parties, check whether institutions have issued formal policies, and see if there are reproducible logs or technical writeups about how these agents authenticate and act in platforms like Canvas. Be skeptical of marketing language that emphasizes complete automation without describing limits or safeguards.

If you are worried about equity impacts, include students who rely on online education in discussions of any new policy. Ensure that device restrictions, proctoring, or synchronous requirements do not unfairly burden those with caregiving responsibilities, unreliable internet, or disabilities; provide alternatives and documented accommodations.

These steps are general, broadly applicable, and do not require external research to start. They focus on changing assessment design, tightening practical account security, demanding vendor transparency, preserving academic fairness, and protecting students’ access while responding to the emerging risk described in the article.

"free people from the labor of education" — This phrase praises the AI as freeing people from work. It frames learning as labor to be removed, helping the AI/developers' view and hiding harms of removing human effort. It nudges readers to see education mainly as burdensome tasks rather than human development. The words are emotive and one-sided, not balanced by reasons learning matters beyond labor.

"claims the ability to perform student tasks by attending lectures, writing papers, and logging into learning platforms such as Canvas to complete assignments and participate in discussions." — The word "claims" weakens the statement and distances the writer from responsibility, implying doubt without saying why. It frames the AI's action as a boast rather than reporting evidence, which makes skeptical readers accept uncertainty even though specific capabilities are listed. This choice shifts burden to the reader to verify.

"such tools can free people from the labor of education and prompt a rethinking of what learning should be" — The phrase "prompt a rethinking" is vague and soft. It suggests a grand, progressive outcome without explaining who should rethink or how. That softness makes the developer argument sound reasonable and visionary while avoiding concrete changes or costs, favoring the developers’ agenda.

"critics say this perspective misunderstands the purpose of schooling." — This sets up a simple opposition where developers are cast as reformers and critics as merely misunderstanding. It reduces critics to being wrong about purpose, which is a framing that favors the developers and simplifies a complex debate into who is right or wrong.

"part of a broader trend of agentic AIs able to navigate learning management systems and complete coursework without student involvement." — The phrase "without student involvement" is stark and absolutes. It highlights threat and may inflame readers by implying total replacement. That absolute wording supports alarm and helps critics’ concerns appear urgent.

"The MLA warned that rapid integration of generative AI into educational platforms is changing instructor-student relationships, assessment standards, and instructional outcomes" — The word "warned" signals danger and gives the MLA authority. It frames integration as negative change without showing counter-evidence, which biases readers toward seeing AI integration as harmful.

"recommended cooperation among educators, lawmakers, and platform providers to give institutions tools to block such agents." — The verb "block" is strong and action-oriented; it presents restriction as the appropriate solution. This favors defensive policy responses and presumes institution-level control is desirable, supporting institutional power.

"using tools like Einstein constitutes academic fraud and undermines foundational norms of human communication and work online." — The phrase "constitutes academic fraud" is an absolute moral judgment presented as the critic’s claim. It frames the technology in criminal or unethical terms and invokes "foundational norms" to suggest deep cultural harm, thus amplifying the critic’s condemnation.

"could harm nontraditional and underprivileged students who rely on credible online education for access to employment and social mobility." — This sentence highlights possible harms to vulnerable groups, using emotive terms "harm," "underprivileged," and "social mobility." It frames the AI as a threat to equity, which supports an argument against the technology by appealing to concern for the disadvantaged.

"limit device use as one way to preserve human engagement and learning" — The phrase "preserve human engagement" implies human engagement is at risk and positions device limits as protective. That supports restrictive classroom policies and favors a human-centered pedagogical view over technological solutions.

"technical approaches—such as distinguishing bot activity from human activity—to protect educational spaces." — The phrase "to protect" casts educational spaces as endangered and technology as defensive. It frames technical control as legitimate protection, which favors surveillance/monitoring solutions and institutional authority.

"frame the technology as a corrective to institutions seen as primarily transactional credential providers" — The verb "frame" notes developers’ spin, but "corrective" and "transactional credential providers" adopt the developers' critique of universities. This choice helps the developers’ narrative that institutions are failing and need disruption, steering readers toward reformist views.

"pointing to perceived failures within universities and rising unemployment among degree holders as reasons to question existing educational models." — The word "perceived" distances the claim from objective fact but still presents unemployment among degree holders as support. The pairing suggests universities are failing, which supports the developers’ disruptive agenda, and it uses selective negative framing about higher education.

The text expresses several clear emotions through its choice of words and the positions attributed to different groups. Concern appears strongly in phrases like “warned that rapid integration,” “risk these tools pose,” and “academic fraud,” which convey fear about harms to educational integrity and credibility; this fear is pronounced and serves to alarm readers about potential negative consequences. Distrust is present in the developers-versus-critics framing and in language such as “misunderstands the purpose of schooling” and “perceived failures within universities,” indicating skepticism about institutions and about the motives or adequacy of opponents; this distrust is moderate to strong and functions to motivate reevaluation of current practices. Defensiveness and protectiveness emerge in descriptions of educators and the MLA recommending cooperation “to give institutions tools to block such agents” and in calls to “preserve human engagement and learning”; these emotions are reasonably strong and aim to justify safeguarding traditional educational practices. Optimism and ambition appear in the developers’ argument that tools can “free people from the labor of education” and “prompt a rethinking of what learning should be,” signaling hope for change and progress; this positive emotion is moderate and serves to present the technology as liberating and forward-looking. Moral indignation shows through terms like “academic fraud” and warnings about harm to “nontraditional and underprivileged students,” a strong emotion meant to cast the technology as ethically problematic and to generate sympathy for vulnerable learners. Pragmatic concern about fairness and access is more measured but present in references to harm to “those who rely on credible online education for access to employment and social mobility,” aiming to highlight real-world stakes and to persuade readers to care about equity. The emotions guide the reader’s reaction by framing the technology as simultaneously promising and dangerous: hope invites curiosity and support for innovation, while fear, distrust, and indignation push toward caution, regulation, or rejection. Language choices steer readers by amplifying perceived risks (words like “undermines,” “fraud,” “risk”) and amplifying benefits (phrases like “free people,” “corrective”), creating a tension that encourages critical judgment. Persuasive techniques in the writing enhance these emotions. The text sets opposing camps—developers versus critics—which simplifies conflict and heightens emotional contrast, and it uses authoritative sources (the MLA, task force members, professors) to lend weight to warnings, increasing credibility and emotional impact. Repetition of concern-related phrases (warnings about integrity, harm to students, changing relationships) reinforces anxiety and urgency. Comparisons between the technology and established norms (agentic AIs versus human engagement; tools that “free” versus schooling’s “purpose”) frame change as a moral and practical trade-off, making stakes clearer and more emotionally resonant. Descriptive verbs and charged nouns (e.g., “claim,” “warned,” “fraud,” “harm,” “protect”) are chosen over neutral terms to provoke a response. Overall, the piece balances hopeful language about innovation with stronger, more vivid language about danger and harm; this deliberate mix shapes readers toward a cautious stance that recognizes potential benefits but prioritizes concerns about integrity, fairness, and the human elements of education.