Proposed Framework for LLM-Based Feedback in LEED Assignments

This study investigates the potential of large language models (LLMs) to enhance feedback generation for written assignments, with a focus on Leadership in Energy and Environmental Design (LEED) projects. Through a systematized review of 70 studies, we identify key trends, including a predominance of structured (52.63%) and argumentative inputs (21.05%) and iterative feedback mechanisms (64.91%), which align with LEED’s criteria-driven workflows. However, gaps remain in addressing technical inputs (3.51%) and scalable automated feedback systems tailored to LEED-specific needs. To address these challenges, a conceptual framework is proposed, emphasizing advanced natural language understanding, real-time iterative feedback, and automated validation systems to support the structured and technical demands of LEED assignments. While this framework offers potential for improving feedback systems in high-stakes educational contexts, further experimental validation is necessary to assess its effectiveness in practice.