AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory

Q1 Social Sciences Computers and Education Artificial Intelligence Pub Date : 2024-12-01 Epub Date: 2024-12-07 DOI:10.1016/j.caeai.2024.100335
Muhammad Zaim , Safnil Arsyad , Budi Waluyo , Havid Ardi , Muhd. Al Hafizh , Muflihatuz Zakiyah , Widya Syafitri , Ahmad Nusi , Mei Hardiah
{"title":"AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory","authors":"Muhammad Zaim ,&nbsp;Safnil Arsyad ,&nbsp;Budi Waluyo ,&nbsp;Havid Ardi ,&nbsp;Muhd. Al Hafizh ,&nbsp;Muflihatuz Zakiyah ,&nbsp;Widya Syafitri ,&nbsp;Ahmad Nusi ,&nbsp;Mei Hardiah","doi":"10.1016/j.caeai.2024.100335","DOIUrl":null,"url":null,"abstract":"<div><div>This study explores the integration of generative AI into English as a Foreign Language (EFL) teaching preparation within Indonesian higher education, addressing the growing need to understand how emerging technologies can enhance pedagogical practices in a rapidly evolving educational landscape. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, the research provides a robust analytical framework to examine the factors influencing lecturers' adoption of generative AI. The study is particularly relevant as generative AI offers significant potential to improve teaching efficiency and content personalization, yet its adoption presents challenges in aligning outputs with educational standards and maintaining meaningful teacher-student interaction. Using a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from reflective compositions, where lecturers critically evaluated their experiences with generative AI. Structural Equation Modeling (SEM) revealed that performance expectancy and social influence significantly and positively influenced behavioral intention, while effort expectancy had no significant effect. Facilitating conditions, unexpectedly, negatively impacted behavioral intention, likely due to satisfaction with existing resources reducing the perceived necessity for new tools. A strong positive correlation between behavioral intention and actual use behavior demonstrated the critical role of intention in driving adoption. Thematic analysis provided further depth by emphasizing both the benefits and challenges of generative AI, accentuating the importance of balancing its use with human instruction to ensure quality teaching and interaction. The study stresses the need for the strategic integration of generative AI, offering practical and theoretical insights into its adoption and implications for advancing EFL teaching in higher education.</div></div>","PeriodicalId":34469,"journal":{"name":"Computers and Education Artificial Intelligence","volume":"7 ","pages":"Article 100335"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Education Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666920X24001383","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 0

Abstract

This study explores the integration of generative AI into English as a Foreign Language (EFL) teaching preparation within Indonesian higher education, addressing the growing need to understand how emerging technologies can enhance pedagogical practices in a rapidly evolving educational landscape. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, the research provides a robust analytical framework to examine the factors influencing lecturers' adoption of generative AI. The study is particularly relevant as generative AI offers significant potential to improve teaching efficiency and content personalization, yet its adoption presents challenges in aligning outputs with educational standards and maintaining meaningful teacher-student interaction. Using a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from reflective compositions, where lecturers critically evaluated their experiences with generative AI. Structural Equation Modeling (SEM) revealed that performance expectancy and social influence significantly and positively influenced behavioral intention, while effort expectancy had no significant effect. Facilitating conditions, unexpectedly, negatively impacted behavioral intention, likely due to satisfaction with existing resources reducing the perceived necessity for new tools. A strong positive correlation between behavioral intention and actual use behavior demonstrated the critical role of intention in driving adoption. Thematic analysis provided further depth by emphasizing both the benefits and challenges of generative AI, accentuating the importance of balancing its use with human instruction to ensure quality teaching and interaction. The study stresses the need for the strategic integration of generative AI, offering practical and theoretical insights into its adoption and implications for advancing EFL teaching in higher education.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于人工智能的英语教学:通过UTAUT和活动理论将生成式人工智能融入大学教学准备
本研究探讨了在印度尼西亚高等教育中将生成式人工智能整合到英语作为外语(EFL)教学准备中,以解决日益增长的需求,了解新兴技术如何在快速发展的教育环境中增强教学实践。通过采用技术接受和使用统一理论(UTAUT)和活动理论,该研究提供了一个强大的分析框架,以检查影响讲师采用生成式人工智能的因素。这项研究尤其相关,因为生成式人工智能在提高教学效率和内容个性化方面具有巨大潜力,但它的采用在使产出与教育标准保持一致以及保持有意义的师生互动方面提出了挑战。该研究采用混合方法,将结构化问卷的定量数据与反思性作文的定性见解相结合,讲师在反思性作文中批判性地评估了他们对生成式人工智能的体验。结构方程模型(SEM)显示,绩效期望和社会影响对行为意向有显著正向影响,而努力期望对行为意向无显著影响。出乎意料的是,便利的条件对行为意图产生了负面影响,这可能是由于对现有资源的满意度降低了对新工具的感知必要性。行为意向与实际使用行为之间存在强烈的正相关关系,这表明意向在推动采用中起着至关重要的作用。专题分析通过强调生成式人工智能的好处和挑战,强调平衡其使用与人类教学以确保教学质量和互动的重要性,提供了进一步的深度。该研究强调了生成式人工智能的战略整合的必要性,并为其在高等教育中的应用及其对促进英语教学的影响提供了实践和理论见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
16.80
自引率
0.00%
发文量
66
审稿时长
50 days
期刊最新文献
Decoding divides: The role of socioeconomic status and personality traits in AI divides and educational inequality Scaffolding critical thinking with generative AI: Design principles for integrating large language models in higher education Opening the blackbox of LLM-based automated essay scoring: Insights into feature weighting patterns and score validity Engagement in LLM chatbot-supported learning: The pivotal roles of GenAI competency and emotion Play with AI (PL-AI): A play-centered, design-based curriculum for AI literacy in pre-K and kindergarten
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1