A University of Florida Professor Stopped Fighting AI in His Classroom: A Peer-Reviewed Study Followed (opens in new tab)
Dr. Brian Harfe addressed generative AI in student writing by redesigning an essay assignment instead of relying on surveillance or AI detectors. In a 310-essay study, students began with AI-generated drafts and revised them into essays reflecting their own views, with word-level provenance tracked through Grammarly Authorship. The results suggest that assignment design can encourage meaningful engagement and provide more reliable insight into AI use than surveys or detection scores. ## Limits of Surveys and AI Detectors - Surveys are influenced by students’ perceptions of acceptable behavior, fear of penalties, and difficulty recalling how much AI assistance they used. - AI detectors provide probabilistic judgments rather than proof. - Most detectors assess an entire document and cannot identify which passages were AI-generated or how the text developed. - These methods measure the final product, not the writing process. ## An Assignment Built Around AI - In the University of Florida course “Can We Design Better Humans? Should We?”, students had to start with a fully AI-generated essay. - They then revised it to express their own views on human cloning and genetic engineering. - Students could keep, modify, or discard as much of the AI draft as they wanted. - Because AI use was explicitly permitted, the assignment removed the incentive to conceal it. - Grammarly Authorship tracked whether each word was typed by the student, copied from AI, or drawn from another source. - Students submitted authorship reports, allowing Harfe to replay the evolution from AI draft to final essay. ## Findings from 310 Essays - The study included students from seven colleges and more than 100 majors. - Students who wrote more original text generally spent more time completing the assignment, linking time-on-task with deeper revision. - STEM students produced more human-generated text than non-STEM students, although both groups were similarly likely to agree with the AI draft. - Higher-performing students revised AI-generated material more extensively across all disciplines. - Students retained approximately 76% of the AI draft on average. - The roughly 5% who disagreed with the AI’s position revised substantially more, adding more of their own writing. - Only two students submitted the AI draft without edits, despite being explicitly allowed to do so for full credit. ## Implications for Education - Harfe’s exact assignment may not apply to every course, but its underlying principle is broadly useful: incorporate AI into learning activities rather than treating it solely as a threat. - Provenance tools provide a record of writing activity instead of an uncertain verdict about authorship. - The findings challenge the assumption that students will automatically surrender their thinking to AI when given permission to use it. - Students’ willingness to revise appears connected to academic engagement and performance. - Reflective assignments can help students evaluate AI’s strengths, weaknesses, and appropriate future uses. Instructors and institutions should focus less on detecting AI after the fact and more on designing assignments that require students to evaluate, revise, and take responsibility for AI-assisted work.