Feedback from Generative AI: Correlates of student engagement in text revision from 655 classes from primary and secondary school

Writing is fundamental in knowledge-based societies, and engaging students in text revision through feedback is critical for developing students’ writing skills. Automated feedback offers a promising solution to teachers’ time constraints creating feedback. However, prior research indicates that 20 to 71 percent of students receiving feedback do not engage in any text revision. Despite these concerning figures, students’ non-engagement has not received widespread attention, likely due to fragmented evidence from a few grade levels and writing tasks disconnected from regular teaching. Further, whether the issue persists when generative AI generates the feedback is unclear. The present study investigates what percentage of students behaviorally engage with feedback from generative AI in authentic classroom learning contexts. We analyzed data from an educational technology company, including 655 teacher-generated writing tasks involving 14,236 students across grades 1-12. Our findings show that around half of the students did not revise a single character in the text after receiving feedback. The percentage was similar across grade levels, task types, or feedback characteristics. We discuss the importance of including the percentage of engaged students as an additional metric in feedback research to achieve the goal that no student is left behind.