This study explores the problem solving capabilities of ChatGPT and its
prospective applications in standardized test preparation, focusing on the GRE
quantitative exam. Prior research has shown great potential for the utilization
of ChatGPT for academic purposes in revolutionizing the approach to studying
across various disciplines. We investigate how ChatGPT performs across various
question types in the GRE quantitative domain, and how modifying question
prompts impacts its accuracy. More specifically this study addressed two
research questions: 1. How does ChatGPT perform in answering GRE-based
quantitative questions across various content areas? 2. How does the accuracy
of ChatGPT vary with modifying the question prompts? The dataset consisting of
100 randomly selected GRE quantitative questions was collected from the ETS
official guide to GRE test preparation. We used quantitative evaluation to
answer our first research question, and t-test to examine the statistical
association between prompt modification and ChatGPT's accuracy. Results show a
statistical improvement in the ChatGPT's accuracy after applying instruction
priming and contextual prompts to the original questions. ChatGPT showed 84%
accuracy with the modified prompts compared to 69% with the original data. The
study discusses the areas where ChatGPT struggled with certain questions and
how modifications can be helpful for preparing for standardized tests like GRE
and provides future directions for prompt modifications.

ChatGPT 在标准化考试准备中的问题解决能力及其潜在应用进行了探索，重点关注 GRE 数学考试。本研究调查了 ChatGPT 在 GRE 数量领域的不同问题类型上的表现，并研究了修改问题提示对其准确性的影响。研究结果表明，在对原始问题使用指令引导和上下文提示后，ChatGPT 的准确性有了统计上的改进。与原始数据 69% 的准确率相比，修改后的提示下 ChatGPT 的准确率达到了 84%。研究讨论了 ChatGPT 在某些问题上的困难之处以及提示修改对于 GRE 等标准化考试准备的帮助，并提供了未来的研究方向。