The AI Motivation Scale (AIMS): a self-determination theory perspective

被引:0
作者
Li, Jiajing [1 ,2 ]
King, Ronnel B. [3 ]
Chai, Ching Sing [3 ]
Zhai, Xuesong [4 ]
Lee, Vivian W. Y. [2 ]
机构
[1] Beijing Normal Univ, Coll Educ Future, Zhuhai, Peoples R China
[2] Chinese Univ Hong Kong, Ctr Learning Enhancement&Research, Hong Kong, Peoples R China
[3] Chinese Univ Hong Kong, Fac Educ, Hong Kong, Peoples R China
[4] Zhejiang Univ, Coll Educ, Hangzhou, Peoples R China
关键词
Artificial Intelligence; motivation; self-determination theory; measurement validation; ARTIFICIAL-INTELLIGENCE; FIT INDEXES; ENGAGEMENT; ACHIEVEMENT; SCHOOL; VALIDATION; GENDER; MODEL;
D O I
10.1080/15391523.2025.2478424
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Artificial Intelligence (AI) has a profound impact on university teaching and learning. However, there is a lack of instruments for measuring university students' motivation to use AI in their learning. In this study, we developed and validated a questionnaire to measure students' motivation to learn with AI. In Study 1, we developed the AI Motivation Scale (AIMS). Rooted in self-determination theory, the scale measures university students' motivation to learn with AI across five dimensions: intrinsic motivation, identified regulation, introjected regulation, external regulation, and amotivation. Both within-network and between-network validation analyses indicated that the AIMS is psychometrically sound. In Study 2, we used the AIMS to explore whether students' motivation to learn with AI is influenced by their university environment and promotes their engagement in learning with AI. The results showed that motivation to learn with AI mediated the positive relationship between supportive environments and engagement in learning with AI. The study shows that AIMS is a psychometrically sound instrument that can be used to assess university students' motivation to learn with AI. It also sheds light on the pivotal role of motivation to learn with AI in the higher education context.
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页数:22
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