Unleashing ChatGPT's Power: A Case Study on Optimizing Information Retrieval in Flipped Classrooms via Prompt Engineering

IF 4.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2023-10-16 DOI:10.1109/TLT.2023.3324714

Mo Wang;Minjuan Wang;Xin Xu;Lanqing Yang;Dunbo Cai;Minghao Yin

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Abstract

This research project investigates the impact of prompt engineering, a key aspect of chat generative pretrained transformer (ChatGPT), on college students' information retrieval in flipped classrooms. In recent years, an increasing number of students have been using AI-based tools, such as ChatGPT rather than traditional research engines to learn and to complete course assignments. Despite this growing trend, previous research has largely overlooked the influence of prompt engineering on students' use of ChatGPT and effective strategies for improving the quality of information retrieval in learning settings. To address this research gap, this study examines the information quality obtained from ChatGPT in a flipped classroom by evaluating its effectiveness in task completion among 26 novice undergraduates from the same major and cohort. The experimental results provide evidence that proficient mastery of prompt engineering improves the quality of information obtained by students using ChatGPT. Consequently, by acquiring proficiency in prompt engineering, students can maximize the positive impact of ChatGPT, obtain high-quality information, and enhance their learning efficiency in flipped classrooms.

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释放 ChatGPT 的力量：通过提示工程优化翻转课堂信息检索的案例研究

本研究项目调查了提示工程（聊天生成预训练转换器（ChatGPT）的一个关键方面）对翻转课堂中大学生信息检索的影响。近年来，越来越多的学生开始使用 ChatGPT 等人工智能工具，而不是传统的研究引擎来学习和完成课程作业。尽管这一趋势在不断增长，但以往的研究在很大程度上忽视了提示工程对学生使用 ChatGPT 的影响，以及在学习环境中提高信息检索质量的有效策略。为了弥补这一研究空白，本研究通过评估 ChatGPT 在 26 名来自同一专业、同一批次的新手本科生中完成任务的效果，考察了在翻转课堂中从 ChatGPT 中获取信息的质量。实验结果证明，熟练掌握提示工程可提高学生使用 ChatGPT 获取信息的质量。因此，通过熟练掌握提示工程学，学生可以最大限度地发挥 ChatGPT 的积极影响，获得高质量的信息，并提高他们在翻转课堂中的学习效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.