Reconceptualizing Self-Directed Learning in the Era of Generative AI: An Exploratory Analysis of Language Learning

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

IEEE Transactions on Learning Technologies Pub Date : 2024-04-10 DOI:10.1109/TLT.2024.3386098

Belle Li;Curtis J. Bonk;Chaoran Wang;Xiaojing Kou

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Abstract

This exploratory analysis investigates the integration of ChatGPT in self-directed learning (SDL). Specifically, this study examines YouTube content creators’ language-learning experiences and the role of ChatGPT in their SDL, building upon Song and Hill's conceptual model of SDL in online contexts. Thematic analysis of interviews with 19 YouTubers and relevant video contents reveals distinct constructs of ChatGPT-integrated SDL, suggesting a reconceptualization and refinement of the SDL framework in the consideration of generative artificial intelligence (AI). This framework emphasizes critical aspects of utilizing ChatGPT as an SDL tool on two distinct levels: 1) the interactive relationships and interplay between learners’ personal traits and their ongoing learning processes (local) and 2) the evolving nature of SDL in the rapidly advancing landscape of generative AI, with socio-political-cultural foundations of AI constantly shaping the learning environment where SDL occurs (global). The study highlights the potential of ChatGPT as a tool for promoting self-directed language learning (SDLL) and provides implications for the development of learning technologies and research on AI-facilitated SDL.

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在生成式人工智能时代重新认识自主学习：对语言学习的探索性分析

这项探索性分析研究了 ChatGPT 在自主学习（SDL）中的整合。具体而言，本研究以 Song 和 Hill 提出的在线环境下 SDL 概念模型为基础，考察了 YouTube 内容创作者的语言学习体验以及 ChatGPT 在其 SDL 中的作用。通过对 19 位 YouTubers 的访谈和相关视频内容进行主题分析，我们发现了 ChatGPT 在 SDL 中的独特构造，并建议在考虑生成式人工智能（AI）时对 SDL 框架进行重新概念化和完善。该框架强调了将 ChatGPT 作为 SDL 工具的两个不同层面的关键方面：1）学习者的个人特质与他们正在进行的学习过程之间的互动关系和相互作用（局部）；2）SDL 在快速发展的生成式人工智能环境中的演变性质，人工智能的社会政治文化基础不断塑造着 SDL 发生的学习环境（全局）。本研究强调了 ChatGPT 作为促进自主语言学习（SDLL）工具的潜力，并为学习技术的发展和人工智能促进的 SDL 研究提供了启示。

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.