The Feedback Loop Architect

Effective feedback is crucial for deep learning, yet providing timely, personalized guidance remains a challenge in education, especially in science. This paper introduces the “Feedback Loop Architect,” a human-centered GenAI platform designed to create personalized feedback ecologies. The platform addresses the need for iterative feedback by simulating and supporting student-platform interactions. A key contribution is our novel methodology for creating a realistic synthetic dataset capturing the dynamics of feedback dialogues. This dataset, informed by established practices like “Sticky Note Feedback,” serves as a testbed for the platform. We demonstrate how the platform simulates learning progressions, visualizes feedback impact, and provides personalized guidance. Results from a simulation of 450 interactions across 50 students show significant improvements in scores (mean increase: 2.69 points) and answer elaboration. This work provides a framework for researchers developing educational AI, offers insights for managers, and explores ethical considerations in such systems.