Development and psychometric analysis of 5-item Artificial Intelligence Attitude (AIAT-5) for students.

Guogang Xin, Shengqin Yang, Xiaoyu Hou, Ruobing Wang, Shuaishuai Mi, Jipeng Wang, Bing Shen

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Article in BMC psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Guogang Xin *School of Government, Beijing Normal University, No.19 Xin Jie Kou Wai Street, Beijing, 100875, China.

Shengqin Yang *Faculty of Education, University of Macau, Avenida da Universidade, Taipa, Macau, China.

Xiaoyu HouChina Basic Education Quality Monitoring Collaborative Innovation Center, Beijing Normal University, No.19 Xin Jie Kou Wai Street, Beijing, 100875, China.

Ruobing WangFaculty of Education, University of Macau, Avenida da Universidade, Taipa, Macau, China.

Shuaishuai MiFaculty of Education, University of Macau, Avenida da Universidade, Taipa, Macau, China.

Jipeng WangInternational Institute of Chinese Studies, Beijing Foreign Studies University, No. 2 North Xisanhuan Avenue, Haidian District, Beijing, 100089, China.

Bing ShenSchool of Arts and Communication, Beijing Normal University, No.19 Xin Jie Kou Wai Street, Beijing, 100875, China. shenbingart7@bnu.edu.cn.

Funding

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6 · The paper itself

Abstract

In the context of accelerating integration of artificial intelligence (AI) technologies into education, rapidly assessing students' AI attitude is crucial for advancing digital transformation in education.The aim of this study is to develop and validate a new five-item version of the Artificial Intelligence Attitude Scale (AIAT-5). For examining the quality of the scale, a total sample of 1576 middle and high school students (study 1: 858; study 2: 614) from diverse regions in China completed the survey. Results: (1) Study1: AIAT-5 exhibited strong internal consistency (Cronbach's α = 0.923, McDonald's ω = 0.923, Guttman's λ6 = 0.911) and good discrimination (α = 3.201 ~ 4.513). Both EFA and EGA supported a robust unidimensional structure. (2) Study 2: CFA results indicated excellent model fit for unidimensional structure (CFI = 0.993, TLI = 0.976, and RMSEA = 0.028), and all factor loadings ranged from 0.879 to 0.953. Furthermore, AI attitude was positively correlated with knowledge of generative AI, life satisfaction, internet addiction, and epistemic curiosity. Overall, within the specific population of Chinese middle and high school students, the AIAT‑5 demonstrates initial validation as a reliable and practically efficient tool for large‑scale assessment of student AI attitude.

Indexed as

Artificial IntelligenceAttitudePsychometricsStudentsAdolescentChinaFemaleHumansMaleReproducibility of ResultsSurveys and QuestionnairesArtificial intelligenceAttitudeExploratory graph analysisIRTScale developmentStudent

Identifiers

PMID42332832

DOI10.1186/s40359-026-05020-4

PMCPMC13540831

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.