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Modeling Students' Perceptions of Artificial Intelligence Assisted Language Learning

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Title: Modeling Students' Perceptions of Artificial Intelligence Assisted Language Learning
Language: English
Authors: Xin An (ORCID 0000-0002-8080-1005), Ching Sing Chai, Yushun Li, Ying Zhou (ORCID 0000-0001-5736-2094), Bingyu Yang
Source: Computer Assisted Language Learning. 2025 38(5-6):987-1008.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 22
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
High Schools
Descriptors: Educational Trends, Trend Analysis, Second Language Learning, Second Language Instruction, Artificial Intelligence, Social Influences, Technology Integration, Teaching Methods, Computer Software, Junior High School Students, High School Students, Likert Scales, Student Attitudes, Validity, Reliability, Prediction, Learning Processes, Student Motivation, Foreign Countries
Geographic Terms: China
DOI: 10.1080/09588221.2023.2246519
ISSN: 0958-8221
1744-3210
Abstract: To address the emerging trend of language learning with Artificial Intelligence (AI), this study explored junior and senior high school students' behavioral intentions to use AI in second language (L2) learning, and the roles of related technological, social, and motivational factors. An eight-factor survey was constructed using a 5-point Likert scale. A total of 524 valid responses were collected, including 280 responses from junior high school students and 244 from senior high school students. The reliability and validity of the scale were satisfactory. The technological and social factors include effort expectancy, performance expectancy, social influence, facilitating conditions of AI-assisted language learning (AILL), which were hypothesized to predict students' behavioral intention to use AILL with reference to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The motivational factors derived from L2 Motivational Self System theory (i.e. learning experience with AI, cultural interest with AI, and instrumentality-promotion with AI) were hypothesized to be intermediate variables between the technological and social factors and behavioral intention based on the extended UTAUT (UTAUT2). Therefore, UTAUT and the L2 Self System were combined according to UTAUT2 to construct the proposed model in this study, named AILL-Motivation-UTAUT model. The results of the structural equation models of AILL-Motivation-UTAUT showed that performance expectancy, cultural interest, and instrumentality-promotion could predict students' behavioral intention to use AILL for both junior and senior high students; effort expectancy and social influence could predict behavioral intention to use AILL only for junior high students, learning experience with AI could predict behavioral intention to use AILL only for senior high students, while facilitating conditions could not predict behavioral intention to use AILL for either group. The predictive power (80% for senior high students and 74% for junior high students) of the AILL-Motivation-UTAUT model in this research is higher than or equal to that of UTAUT2 (74%). In addition, this study found that the technological and social factors perceived by students would predict the motivation in AILL. The model verified in this study may inform future studies on AI integration for English as foreign language learning.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1479193
Database: ERIC
FullText Text:
  Availability: 0
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PubType: Academic Journal
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  Data: Modeling Students' Perceptions of Artificial Intelligence Assisted Language Learning
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  Data: <searchLink fieldCode="AR" term="%22Xin+An%22">Xin An</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8080-1005">0000-0002-8080-1005</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ching+Sing+Chai%22">Ching Sing Chai</searchLink><br /><searchLink fieldCode="AR" term="%22Yushun+Li%22">Yushun Li</searchLink><br /><searchLink fieldCode="AR" term="%22Ying+Zhou%22">Ying Zhou</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5736-2094">0000-0001-5736-2094</externalLink>)<br /><searchLink fieldCode="AR" term="%22Bingyu+Yang%22">Bingyu Yang</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Computer+Assisted+Language+Learning%22"><i>Computer Assisted Language Learning</i></searchLink>. 2025 38(5-6):987-1008.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 22
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
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  Data: 10.1080/09588221.2023.2246519
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  Data: 0958-8221<br />1744-3210
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To address the emerging trend of language learning with Artificial Intelligence (AI), this study explored junior and senior high school students' behavioral intentions to use AI in second language (L2) learning, and the roles of related technological, social, and motivational factors. An eight-factor survey was constructed using a 5-point Likert scale. A total of 524 valid responses were collected, including 280 responses from junior high school students and 244 from senior high school students. The reliability and validity of the scale were satisfactory. The technological and social factors include effort expectancy, performance expectancy, social influence, facilitating conditions of AI-assisted language learning (AILL), which were hypothesized to predict students' behavioral intention to use AILL with reference to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The motivational factors derived from L2 Motivational Self System theory (i.e. learning experience with AI, cultural interest with AI, and instrumentality-promotion with AI) were hypothesized to be intermediate variables between the technological and social factors and behavioral intention based on the extended UTAUT (UTAUT2). Therefore, UTAUT and the L2 Self System were combined according to UTAUT2 to construct the proposed model in this study, named AILL-Motivation-UTAUT model. The results of the structural equation models of AILL-Motivation-UTAUT showed that performance expectancy, cultural interest, and instrumentality-promotion could predict students' behavioral intention to use AILL for both junior and senior high students; effort expectancy and social influence could predict behavioral intention to use AILL only for junior high students, learning experience with AI could predict behavioral intention to use AILL only for senior high students, while facilitating conditions could not predict behavioral intention to use AILL for either group. The predictive power (80% for senior high students and 74% for junior high students) of the AILL-Motivation-UTAUT model in this research is higher than or equal to that of UTAUT2 (74%). In addition, this study found that the technological and social factors perceived by students would predict the motivation in AILL. The model verified in this study may inform future studies on AI integration for English as foreign language learning.
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        Value: 10.1080/09588221.2023.2246519
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      – Text: English
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      – SubjectFull: Second Language Learning
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