How Adding Metacognitive Requirements in Support of AI Feedback in Practice Exams Transforms Student Learning Behaviors
Mak Ahmad, Prerna Ravi, David Karger, Marc Facciotti

TL;DR
This study evaluates an AI-supported practice exam system that promotes metacognitive reflection, showing high student engagement and confidence gains, with potential benefits from structured reflection over feedback complexity.
Contribution
The paper introduces a large-scale, empirically tested AI feedback system that integrates metacognitive prompts and textbook references to enhance student learning behaviors.
Findings
High student engagement with textbook references (40%)
Increased student confidence and concept recall
No significant performance difference across feedback types
Abstract
Providing personalized, detailed feedback at scale in large undergraduate STEM courses remains a persistent challenge. We present an empirically evaluated practice exam system that integrates AI generated feedback with targeted textbook references, deployed in a large introductory biology course. Our system encourages metacognitive behavior by asking students to explain their answers and declare their confidence. It uses OpenAI's GPT-4o to generate personalized feedback based on this information, while directing them to relevant textbook sections. Through interaction logs from consenting participants across three midterms (541, 342, and 413 students respectively), totaling 28,313 question-student interactions across 146 learning objectives, along with 279 surveys and 23 interviews, we examined the system's impact on learning outcomes and engagement. Across all midterms, feedback types…
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