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

被引:43
|
作者
Wang, Mo [1 ]
Wang, Minjuan [2 ]
Xu, Xin [1 ]
Yang, Lanqing [1 ]
Cai, Dunbo [3 ]
Yin, Minghao [1 ]
机构
[1] Northeast Normal Univ, Changchun 130024, Peoples R China
[2] San Diego State Univ, Learning Design & Technol, San Diego, CA 92182 USA
[3] China Mobile Suzhou Software Technol Co Ltd, Ctr Technol Res & Innovat, Suzhou 215000, Peoples R China
来源
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES | 2024年 / 17卷
基金
中国国家自然科学基金;
关键词
Chat generative pretrained transformer (ChatGPT); flipped classrooms; information retrieval; prompt engineering; CHATBOTS;
D O I
10.1109/TLT.2023.3324714
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:629 / 641
页数:13
相关论文
共 3 条
  • [1] A library's information retrieval system (In) effectiveness: case study
    Marijan, Robert
    Leskovar, Robert
    LIBRARY HI TECH, 2015, 33 (03) : 369 - 386
  • [2] Optimizing Human-AI Collaboration in Chemistry: A Case Study on Enhancing Generative AI Responses through Prompt Engineering
    Vidhani, Dinesh V.
    Mariappan, Manoharan
    CHEMISTRY-SWITZERLAND, 2024, 6 (04): : 723 - 737
  • [3] Optimizing Mobile-Edge AI-Generated Everything (AIGX) Services by Prompt Engineering: Fundamental, Framework, and Case Study
    Liu, Yinqiu
    Du, Hongyang
    Niyato, Dusit
    Kang, Jiawen
    Cui, Shuguang
    Shen, Xuemin
    Zhang, Ping
    IEEE NETWORK, 2024, 38 (05): : 220 - 228