Providing high-quality feedback on student assignments is crucial for student success, but it is heavily limited by time and budgetary constraints. In this work, we introduce Synthetic Educational Feedback Loops (SEFL), a synthetic data framework designed to generate data that resembles immediate, on-demand feedback at scale without relying on extensive, real-world student assignments and teacher feedback. To obtain this type of data, two large language models (LLMs) operate in a teacher-student role to simulate assignment completion and formative feedback, generating 19.8K synthetic pairs of student work and corresponding critiques and actionable improvements from a teacher. With this data, we fine-tune smaller, more computationally efficient LLMs on these synthetic pairs, enabling them to replicate key features of high-quality, goal-oriented feedback. Through comprehensive evaluations with three LLM judges and three human experts, across a subset of 900 outputs, we demonstrate that SEFL-tuned models outperform both their untuned counterparts and an existing baseline in terms of feedback quality. The potential for societal impact is reinforced by extensive qualitative comments and ratings from human stakeholders -- both students and higher education instructors. SEFL has the potential to transform feedback processes for higher education and beyond.

翻译：为学生作业提供高质量反馈对于学生成功至关重要，但这一过程严重受限于时间和预算约束。本研究提出合成教育反馈循环（SEFL），这是一种合成数据框架，旨在无需依赖大量真实学生作业和教师反馈的情况下，大规模生成类似于即时按需反馈的数据。为获得此类数据，两个大型语言模型（LLMs）以师生角色运作，模拟作业完成过程和形成性反馈，从而生成19.8K个合成数据对，包含学生作业及相应的教师评析与可操作性改进建议。利用这些数据，我们在这些合成数据对上对更小、计算效率更高的LLMs进行微调，使其能够复现高质量、目标导向反馈的关键特征。通过三位LLM评审员和三位人类专家对900份输出样本进行的全面评估，我们证明SEFL微调模型在反馈质量方面均优于未微调的原始模型及现有基线方法。来自学生和高等教育教师等多方人类利益相关者的广泛定性评论与评分，进一步强化了该方法的社会影响潜力。SEFL有望变革高等教育乃至更广泛领域的反馈流程。