The Effects of AI Feedback on College Students’ Reading and Writing Performance in an Intelligent Text Framework

Abstract

This study investigates how AI feedback influences college students’ reading and writing performance within an intelligent text aligned with the Interactive Constructive Active and Passive (ICAP) framework. Using a within-subjects design, students completed two read-to-write tasks — constructed response items (CRIs) and summaries — under two conditions: (1) Strategic Thinking And Interactive Reading Support (STAIRS), which provided AI feedback to support interactive engagement, and (2) Random Reread, a control condition without AI feedback to support constructive engagement. STAIRS offered automated scoring of read-to-write tasks, targeted rereading, and a chatbot that engaged students in interactive dialogues to support revision. Results showed that STAIRS significantly improved students’ summary revisions and subsequent summary performance, suggesting that AI feedback functions not only as guidance for improving current summaries but also benefits future performance. However, students in the STAIRS condition did not show higher CRI or summative quiz scores. CRI scores may have been limited by a ceiling effect, while the lack of quiz benefits may reflect a tradeoff in the study design, where the classroom tasks did not fully match the system’s activities. These mixed outcomes highlight the need to further explore how AI feedback may support different aspects of reading with an intelligent text.

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