AI 中文总结
本研究开发并验证了面向高中生的GenAIT测试，其18道题覆盖GenAI多领域知识，经大样本验证适用于群体研究，且发现AI使用频率与概念理解负相关。
AI 中文摘要
国际上对高中生的生成式AI（GenAI）素养及其评估的兴趣日益增长，但针对该群体的客观评估仍有待完善。本文报告了GenAI素养测试（GenAIT）的迭代开发与验证过程，该测试共18道单项选择题，用于评估高中生关于GenAI的概念性知识，内容涵盖技术、实践和人类影响三个领域。专家对测试的相关性、清晰度和全面性进行了评审，为内容效度提供了证据。在对7432名爱沙尼亚高中生开展的大规模调查中，研究人员通过验证性因子分析、经典测验理论和项目反应理论，评估了爱沙尼亚语版本GenAIT的心理测量学特性。结果支持近似单维性，该测试用于群体研究的可靠性大致充足（边缘可靠性=.72，KR-20=.69），且三参数逻辑模型拟合良好（RMSEA=.013，TLI=.987，CFI=.990，SRMSR=.021）。测量精度对大多数学生而言足够，但随潜在特质存在显著差异，得分较低的学生精度更低。因此，GenAIT更适合群体研究，而非高风险的个体分类。GenAIT得分与感知有用性、感知易用性无关，与大语言模型（LLM）使用频率呈负相关，这表明频繁使用AI和对AI的积极认知不应被视为概念理解的替代指标。
英文摘要
There is growing international interest in generative AI (GenAI) literacy and its assessment among high school students, but objective assessment in this population remains underdeveloped. This article reports the iterative development and validation of the GenAI Literacy Test (GenAIT), an 18-item multiple-choice test measuring high school students' conceptual knowledge about GenAI, with content spanning technical, practical, and human-impact domains. Expert review of relevance, clarity, and comprehensiveness provided evidence of content validity. In a large-scale survey of 7432 Estonian high school students, we evaluated the psychometric functioning of the Estonian-language GenAIT using confirmatory factor analysis, classical test theory, and item response theory. Results supported approximate unidimensionality, broadly adequate reliability for group-level research (marginal reliability = .72, KR-20 = .69), and good fit of a three-parameter logistic model (RMSEA = .013, TLI = .987, CFI = .990, SRMSR = .021). Measurement precision was sufficient for the majority of students but varied substantially across the latent trait, with lower precision for lower scoring students. GenAIT is therefore more suitable for group-level research than high-stakes individual classification. GenAIT scores were unrelated to perceived usefulness and perceived ease of use, and negatively associated with LLM use frequency, suggesting that frequent use and favorable perceptions of AI should not be treated as proxies for conceptual understanding.
