Fostering L2 Learners' Pronunciation and Motivation via Affordances of Artificial Intelligence

被引:3
作者
Shafiee Rad, Hanieh [1 ]
Roohani, Ali [1 ]
机构
[1] Shahrekord Univ, English Dept, Shahr E Kord, Iran
关键词
AI; L2; Pronunciation; sequential explanatory mixed-methods; motivation; AI;
D O I
10.1080/07380569.2024.2330427
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Individualized pronunciation teaching is propitious for foreign/second language (L2) learners, and artificial intelligence (AI) applications are one of the most promising methods for achieving personalized instruction. Through a sequential explanatory mixed method, the current study sought to investigate the impact of using an AI application on L2 learners' pronunciation and attitudes. To accomplish this goal, two intact classes from an English language school, in one of the cities in Iran where the authors reside, were randomly chosen and divided into experimental (N = 23) and control (N = 25) groups. Alongside instructor's lecture, the L2 learners in the experimental group were required to complete individualized pronunciation tasks at home using an AI-enhanced application. Pre-, post-, and delayed posttests on pronunciation were conducted to collect data on pronunciation gain. Semi-structured interviews were utilized to gather more in-depth information from the learners and explore their perceptions regarding the use of the AI-enhanced app. Two-way mixed ANOVA results demonstrated that the AI-enhanced group outperformed the conventional teacher-fronted group in pronunciation learning. Thematic analysis also indicated that the learners using the application had more positive attitudes. It is suggested that AI-based applications be utilized as potential mediums for enhancing the pronunciation learning process, especially in individualized learning.
引用
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页数:22
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共 58 条
[21]  
Hwang G.-J., 2020, Computers and Education: Artificial Intelligence, V1, DOI DOI 10.1016/J.CAEAI.2020.100001
[22]   Assessment of L2 intelligibility: Comparing L1 listeners and automatic speech recognition [J].
Inceoglu, Solene ;
Chen, Wen-Hsin ;
Lim, Hyojung .
RECALL, 2023, 35 (01) :89-104
[23]   Exploring AI chatbot affordances in the EFL classroom: young learners' experiences and perspectives [J].
Jeon, Jaeho .
COMPUTER ASSISTED LANGUAGE LEARNING, 2024, 37 (1-2) :1-26
[24]   Evaluating an Artificial Intelligence Literacy Programme for Developing University Students? Conceptual Understanding, Literacy, Empowerment and Ethical Awareness [J].
Kong, Siu-Cheung ;
Cheung, William Man-Yin ;
Zhang, Guo .
EDUCATIONAL TECHNOLOGY & SOCIETY, 2023, 26 (01) :16-30
[25]  
Kuddus K., 2022, Advanced analytics and deep learning models, P1
[26]   Visualizing a disembodied agent: young EFL learners' perceptions of voice-controlled conversational agents as language partners [J].
Lee, Seongyong ;
Jeon, Jaeho .
COMPUTER ASSISTED LANGUAGE LEARNING, 2024, 37 (5-6) :1048-1073
[27]   Artificial intelligence applications in psychoradiology [J].
Li, Fei ;
Sun, Huaiqiang ;
Biswal, Bharat B. ;
Sweeney, John A. ;
Gong, Qiyong .
PSYCHORADIOLOGY, 2021, 1 (02) :94-107
[28]   The effects of articulatory gestures on L2 pronunciation learning: A classroom-based study [J].
Li, Ying ;
Somlak, Taylor .
LANGUAGE TEACHING RESEARCH, 2019, 23 (03) :352-371
[29]   Generative AI and the future of education: Ragnarok or reformation? A paradoxical perspective from management educators [J].
Lim, Weng Marc ;
Gunasekara, Asanka ;
Pallant, Jessica Leigh ;
Pallant, Jason Ian ;
Pechenkina, Ekaterina .
INTERNATIONAL JOURNAL OF MANAGEMENT EDUCATION, 2023, 21 (02)
[30]  
Lin CJ, 2021, EDUC TECHNOL SOC, V24, P16