The universal availability of ChatGPT and other similar tools since late 2022 has prompted tremendous public excitement and experimental effort about the potential of large language models (LLMs) to improve learning experience and outcomes, especially for learners from disadvantaged backgrounds. However, little research has systematically examined the real-world impacts of LLM availability on educational equity beyond theoretical projections and controlled studies of innovative LLM applications. To depict trends of post-LLM inequalities, we analyze 1,140,328 academic writing submissions from 16,791 college students across 2,391 courses between 2021 and 2024 at a public, minority-serving institution in the US. We find that students' overall writing quality gradually increased following the availability of LLMs and that the writing quality gaps between linguistically advantaged and disadvantaged students became increasingly narrower. However, this equitizing effect was more concentrated on students with higher socioeconomic status. These findings shed light on the digital divides in the era of LLMs and raise questions about the equity benefits of LLMs in early stages and highlight the need for researchers and practitioners on developing responsible practices to improve educational equity through LLMs.

翻译：自2022年底以来，ChatGPT及类似工具的普遍可及性引发了公众的巨大热情和实验探索，人们普遍关注大型语言模型（LLMs）在改善学习体验与成果方面的潜力，特别是对弱势背景学习者的助益。然而，现有研究大多局限于理论预测和创新性LLM应用的受控实验，鲜有系统考察LLM可及性对教育公平的实际影响。为描绘后LLM时代的不平等趋势，我们分析了美国一所公立少数族裔服务院校在2021年至2024年间，来自2,391门课程、16,791名大学生的1,140,328份学术写作提交。研究发现，自LLMs普及后，学生整体写作质量逐步提升，且语言优势学生与语言劣势学生之间的写作质量差距持续收窄。然而，这种平等化效应更集中于社会经济地位较高的学生群体。这些发现揭示了LLM时代的数字鸿沟，对早期阶段LLMs的公平效益提出质疑，并强调研究者与实践者需制定负责任的应用实践，以通过LLMs促进教育公平。