Development and validation of the AI literacy, risk perception, and academic confidence questionnaire for Chinese pre-service teachers

Zeyu Zhang, Xiaomei Lu, Guochao Xiao and Xinde Wang

PLOS ONE, 2026, vol. 21, issue 7, 1-14

Abstract: Artificial intelligence (AI) is becoming increasingly relevant to teacher education, yet evidence remains limited on how pre-service teachers’ AI literacy, risk perception, and academic confidence can be assessed within a coherent but multidimensional framework. This study examined the psychometric properties of the AI Literacy, Risk Perception, and Academic Confidence Questionnaire (AIRPAC-Q) among Chinese pre-service teachers. A cross-sectional survey was conducted with 528 participants recruited from teacher education programmes in China. The sample was randomly divided into an exploratory factor analysis (EFA) subsample (n = 258) and a confirmatory factor analysis (CFA) subsample (n = 270). Psychometric evaluation included expert-based content validation, pilot refinement, EFA, CFA, reliability testing, convergent and discriminant validity, concurrent validity, and known-group validity analyses. The final questionnaire retained 14 items across three complementary dimensions: AI literacy, risk perception, and academic confidence. Expert ratings showed acceptable content validity (I-CVI = 0.83–1.00; S-CVI/Ave = 0.94). The hypothesized three-factor model showed an acceptable fit to the data (χ² = 146.32, df = 74, χ²/df = 1.98, CFI = 0.952, TLI = 0.941, RMSEA = 0.060, SRMR = 0.047) and outperformed two-factor and one-factor alternatives. Cronbach’s α values ranged from 0.82 to 0.88, composite reliability values ranged from 0.83 to 0.89, and average variance extracted values ranged from 0.56 to 0.59. Participants with prior AI use experience scored higher on AI literacy and academic confidence but slightly lower on risk perception than those without such experience. These findings support the AIRPAC-Q as a context-specific multidimensional tool for assessing competence, caution, and confidence in AI-supported teacher education.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0353837

DOI: 10.1371/journal.pone.0353837

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