Teacher Strategies for Reducing AI Generated Content in English Major Student Writing
Abstract
Generative AI can produce fluent prose before students have worked through the linguistic choices that writing courses are intended to teach. This study examined how one university instructor responded to that problem in a second-year English-major writing course. Forty-two students (18 men and 24 women; mean age = 20.3 years) participated in a 16-week concurrent embedded mixed-methods study. The instructor compared AI-probability estimates from GPTZero, Originality.AI, and Turnitin, returned passage-level feedback, and taught four sessions on making context-sensitive lexical choices without asking AI to rewrite students' work. Questionnaires, interviews with six students, three sets of writing samples, and course scores supplied the data. Across Weeks 5, 10, and 15, the mean estimated share of AI-generated text fell from 51.2% to 35.8% and then to 23.6%. Overall AI-tool use also declined, while mean writing scores rose from 72.4 to 85.9. Students most often associated AI use with difficulty expressing ideas accurately, limited time, and pressure to obtain higher grades. By the end of the semester, more students distinguished grammar assistance from having AI compose a paper. Because the study involved one class and no control group, these changes should be read as associations rather than proof of a causal effect. Even so, the findings illustrate how transparent checking, individualized revision, and lexical instruction can be combined in a classroom response to AI-dependent writing.
