AI Agents Can Now Navigate and Complete LMS Tasks: A Call for Pedagogical Innovation

Abstract

Autonomous artificial intelligence (AI) agents can now log into a learning management system, read course materials, and complete unproctored, asynchronous assessed work end-to-end with no student involvement. We document that capability and trace its consequences for assessment validity. Three demonstrations on a live undergraduate course supply the evidence: two quiz completions, one in approximately 12 minutes, one in under 5, and a third in which the agent fabricated credible personal reflection for a discussion board. The wider public record includes at least 15 documented agent runs across three platforms and seven tools. We apply Kane’s argument-based validity framework: agent completion removes the attribution on which every inference in Kane’s chain depends. Everything downstream, from course grades to the evidence chains behind program review and accreditation, rests on support that is no longer there. The failure concerns validity rather than integrity: an institution can punish misconduct and still lack grounds for the scores it reports. Collective accreditor guidance addresses institutional uses of AI in evaluation and does not yet reach the agentic case. Audience data from the underlying conference session show attendees already recognizing both the vulnerability and the gap in institutional guidance. Polled attendees most often named online quizzes as agent-completable, with discussion-based work close behind. Majorities in both listings were working without settled written guidance. The response defended here is design rather than detection: four principles for verified human presence, low-effort changes faculty can adopt now, and the assurance levers assessment professionals already operate.