AI-scaffolded summary writing for pre-class learning in an undergraduate physics course
Min Kyu Kim, Hyunkyu Han, Seora Kim, Mohamed Shameer Abdeen · Assessing Writing · 2026
Summary writing is a common write-to-learn strategy in undergraduate STEM education, particularly for pre-class learning. Yet producing summaries that demonstrate deep comprehension is demanding and often requires instructional support. In response, Automated Summary Evaluation (ASE) tools have been developed to provide formative assessment and feedback on student summaries. This study examines the effectiveness of a generative AI-powered ASE tool designed to scaffold student engagement in pre-class summarization. We investigated whether AI support enhances editing behaviors and promotes concept learning while interacting with learner background characteristics. We analyzed 1081 revision attempts across seven topics over seven weeks from 49 undergraduates in an Introductory Physics course at a large public university. A longitudinal analytic approach using Linear Mixed-Effects Models was employed to address the research questions. Findings indicate that AI-powered formative feedback fostered revision behaviors associated with higher concept learning scores. Effective revisions occurred when students added concepts in response to AI feedback while avoiding careless deletions and surface-level sentence changes. Engagement and performance varied more by assignment than by tool proficiency, with AI scaffolds especially beneficial for students historically underperforming in STEM. These results underscore the importance of personalized feedback strategies that promote targeted revision across diverse learners.