L2 学习中的人工智能:对情境、教学和社会情感调节因素的元分析

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL ACS Applied Energy Materials Pub Date : 2024-09-23 DOI:10.1016/j.system.2024.103498
Xiu-Yi Wu
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引用次数: 0

摘要

本研究对人工智能(AI)干预措施在语言学习中的有效性进行了全面的荟萃分析,并探讨了各种情境、教学和社会情感因素的调节作用。通过对包含 79 份报告的 49 项研究的数据进行综合分析,分析结果表明,人工智能干预措施对语言学习效果有显著的积极影响。研究确定了影响人工智能效果的几个关键调节因素,包括学习环境、干预持续时间、教育水平、参与者年龄、样本大小、目标语言技能、互动类型和反馈来源。研究结果表明,在线和混合式学习环境、中等持续时间的干预(6 周至 6 个月)、高等教育环境和年轻的成人学习者显示出最高的有效性。此外,人工智能干预在提高听力和口语技能方面最为有效,在写作和词汇掌握方面也有显著效果。虽然参与度和满意度存在差异,但人工智能对动机、焦虑减少和交流意愿等社会情感因素产生了积极影响。这项研究强调了个性化和自适应人工智能工具的重要性,利用自动和人工反馈最大限度地提高学习效果。这些见解为越来越多的人工智能教育文献做出了贡献,为优化人工智能增强型教育工具提供了指导,从而有效地支持不同的学习者。未来的研究应进一步调查学习者之间的个体差异,以便更精确地定制人工智能干预措施,增强其教育效果。
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Artificial Intelligence in L2 learning: A meta-analysis of contextual, instructional, and social-emotional moderators
This study conducts a comprehensive meta-analysis to examine the effectiveness of artificial intelligence (AI) interventions in language learning and to explore the moderating effects of various contextual, instructional, and social-emotional factors. By synthesizing data from 49 studies comprising 79 reports, the analysis reveals that AI interventions have a significant positive impact on language learning outcomes. The study identifies several key moderators influencing AI effectiveness, including the learning environment, intervention duration, educational level, participants’ age, sample size, language skills targeted, interactivity types, and feedback sources. The findings indicate that online and blended learning environments, medium-duration interventions (6 weeks–6 months), higher education settings, and young adult learners show the highest effectiveness. Additionally, AI interventions are most effective in enhancing listening and speaking skills, with significant benefits also observed for writing and vocabulary acquisition. Social-emotional factors such as motivation, anxiety reduction, and willingness to communicate are positively influenced by AI, though engagement and satisfaction exhibit variability. The study underscores the importance of personalized and adaptive AI tools, leveraging both automated and human feedback to maximize learning outcomes. These insights contribute to the growing body of literature on AI in education, offering guidance for optimizing AI-enhanced educational tools to support diverse learners effectively. Future research should further investigate individual differences among learners to tailor AI interventions more precisely and enhance their educational impact.
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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