Flatness and Skewness Status of Variables.

This study examines Generative Artificial Intelligence (GenAI) acceptance and Artificial Intelligence Literacy (AIL) levels among prospective teachers, using variables for comparative analysis and identifying influencing factors. The research uses an explanatory sequential mixed methods approach. Quantitative data were obtained from 723 prospective teachers and qualitative data from 48 prospective teachers. Data collection included an Information Form, GenAI Acceptance Scale, and AIL Scale for quantitative data, with interview forms for qualitative data. Parametric tests, independent samples t-test, ANOVA, and Pearson correlation analyzed quantitative data, while factors influencing GenAI acceptance and AIL were identified through themes using MAXQDA. Acceptance levels showed no significant differences by gender or daily internet use; however, differences emerged regarding department, grade level, AI tools used, and self-perceived proficiency. AIL showed significant differences in gender, department, grade, tool usage, and proficiency level, with higher scores among those trained in artificial intelligence. Qualitative data clarify the quantitative findings. Factors affecting GenAI acceptance include daily use, problem-solving, learning applications, mentor usage, assistance from others, proficiency, productivity, discipline-specific skills, and task efficiency. Factors influencing AIL include understanding AI importance, ethical considerations, AI support in daily life, explaining AI, understanding deep learning and machine learning relationships, big data knowledge, AI decision-making processes, knowledge of AI tools, interpretation of AI technologies, critical evaluation, data privacy importance, machine learning knowledge, and evaluation of AI applications in their discipline.

本研究旨在考察准教师群体对生成式人工智能（Generative Artificial Intelligence，GenAI）的接受度与人工智能素养（Artificial Intelligence Literacy，AIL）水平，通过设置变量开展对比分析并识别相关影响因素。本研究采用解释性序列混合研究方法。本研究从723名准教师处采集定量数据，从48名准教师处采集定性数据。定量数据采集工具包括个人信息调查表、生成式人工智能接受度量表（GenAI Acceptance Scale）及人工智能素养量表（AIL Scale），定性数据则通过访谈问卷采集。定量数据采用参数检验、独立样本t检验、方差分析（ANOVA）及皮尔逊相关分析进行处理；本研究借助MAXQDA软件通过主题编码识别生成式人工智能接受度与人工智能素养的影响因素。研究结果显示，准教师对生成式人工智能的接受度在性别与每日互联网使用维度上均无显著差异，但在所在院系、年级、所使用的人工智能工具类型及自我感知熟练度方面存在显著差异。人工智能素养则在性别、所在院系、年级、工具使用情况及熟练度层面均存在显著差异，且接受过人工智能相关培训的群体得分更高。定性数据进一步阐释了定量研究的结果。影响生成式人工智能接受度的因素包括日常使用频率、问题解决能力、学习应用场景、导师使用情况、他人协助、熟练度、生产力提升、学科专属技能及任务效率。影响人工智能素养的因素包括对人工智能重要性的认知、伦理考量、人工智能在日常生活中的支持作用、人工智能的解释性说明、深度学习（deep learning）与机器学习（machine learning）的关系认知、大数据（big data）知识、人工智能决策流程认知、人工智能工具知识、人工智能技术解读、批判性评价、数据隐私重要性认知、机器学习知识以及对所在学科领域内人工智能应用的评价。