Human Error Is the Point: On Teaching College During the Rise of AI (therumpus.net)

An essay by a college instructor argues that, amid AI’s growing role in education, the messy, error-ridden, embodied work of teaching remains indispensible. The author describes everyday slips—forgotten lanyards, misheard words, awkward silences, and offhand questions—that create the friction where real learning happens: a student’s misphrasing leads to an office-hour story; a quiet return of voice shifts the whole room. While AI can generate slides, sort posts, and optimize scaffolding, it cannot register the small vocal shifts, glances, stutters, or the “mishearing” that opens doors to meaning. Those unmeasurable moments—embarrassment turned insight, the shared laugh that reorients a class—aren’t captured by inputs/outputs or A/B tests. For the AI/ML community, the essay is a cautionary reminder and design provocation: educational models can augment logistics and content delivery, but they can’t replicate presence, silence, or the nuanced social cues that enable vulnerability and discovery. This has practical implications—datasets and metrics won’t fully capture pedagogical value; evaluation must consider nonquantifiable outcomes; and practitioner tools should be designed to support, not replace, human teachers. The human error the author celebrates is not a flaw to be eliminated but a pedagogical affordance AI should respect.