Write-Curate-Verify: A Case Study of Leveraging Generative AI for Scenario Writing in Scenario-Based Learning

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2024-03-18 DOI:10.1109/TLT.2024.3378306

Shurui Bai;Donn Emmanuel Gonda;Khe Foon Hew

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Abstract

This case study explored the use of generative artificial intelligence (GenAI), specifically chat generative pretraining transformer (ChatGPT), in writing scenarios for scenario-based learning (SBL). Our research addressed three key questions: 1) how do teachers leverage GenAI to write scenarios for SBL purposes? 2) what is the quality of GenAI-generated SBL scenarios and tasks? and 3) how does GenAI-supported SBL affect students’ motivation, learning performance, and learning perceptions? A three-step prompting engineering process (write the prompts, curate the output, and verify the output, WCV) was established during the teacher interaction with GenAI in the scenario writing. Findings revealed that by using the WCV approach, ChatGPT enabled the efficient creation of quality scenarios for SBL purposes in a short timeframe. Moreover, students exhibited increased intrinsic motivation, learning performance, and positive attitudes toward GenAI-supported scenarios. We also suggest guidelines for using the WCV prompt engineering process in scenario writing.

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编写-评测-验证：在情景式学习中利用生成式人工智能进行情景写作的案例研究

本案例研究探讨了生成式人工智能（GenAI），特别是聊天生成式预训练转换器（ChatGPT）在情景式学习（SBL）情景编写中的应用。我们的研究解决了三个关键问题：1) 教师如何利用 GenAI 为 SBL 编写情景？2）GenAI 生成的 SBL 情景和任务的质量如何？ 3）GenAI 支持的 SBL 如何影响学生的学习动机、学习成绩和学习感知？在教师与 GenAI 的交互过程中，建立了三步提示工程流程（编写提示、策划输出、验证输出，WCV）。研究结果表明，通过使用 WCV 方法，ChatGPT 能够在短时间内为 SBL 目的高效创建高质量的情景。此外，学生对 GenAI 支持的情景模式表现出更高的内在动力、学习成绩和积极态度。我们还提出了在情景写作中使用 WCV 提示工程流程的指导原则。

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.