Human–AI visual co-creation dataset: critical thinking and AI literacy in higher education

This dataset contains the anonymised data collected for the study “Human–AI visual co-creation as a pedagogical approach for developing critical thinking and AI literacy in higher education”. The data were gathered from 116 undergraduate students enrolled in Early Years Education programmes at two Spanish universities. The dataset includes responses to a mixed-methods questionnaire designed to analyse students’ perceptions of artificial intelligence in a visual co-creation context. Variables cover four main analytical dimensions: (1) AI interpretative capacity (visual recognition vs. semantic interpretation), (2) perceptual shift in relation to students’ own work, (3) authorship and aesthetic evaluation, and (4) critical understanding of AI systems, including the identification of limitations and the role of prompts. Quantitative variables are coded in ordinal and nominal formats, while qualitative responses are provided in their original textual form (translated into English where applicable). The dataset supports the statistical analyses reported in the study (descriptive statistics, Spearman correlations, and chi-square tests), as well as the qualitative coding process conducted using thematic analysis. All data have been fully anonymised in accordance with ethical research standards and approved institutional procedures. The dataset is intended to support transparency, reproducibility, and further research on human–AI interaction, media literacy, and technology-enhanced learning in higher education contexts.本数据集收录了题为《以人机视觉共创作为高等教育阶段培养批判性思维与人工智能（AI）素养的教学方法》的研究中采集的匿名化数据。数据采集自西班牙两所高校修读早期教育专业的116名本科生。 本数据集包含针对混合方法问卷的作答结果，该问卷旨在分析学生在视觉共创场景下对人工智能的认知态度。研究变量涵盖四大核心分析维度：(1) 人工智能阐释能力（视觉识别与语义解读）；(2) 针对学生自身创作的认知转变；(3) 创作归属与审美评价；(4) 对人工智能系统的批判性认知，包括识别其局限性及提示词（prompt）的作用。 定量变量以有序分类与名义分类格式进行编码，而定性作答则保留原始文本形式（适用时已译为英文）。本数据集可支撑本研究中报告的各类统计分析，包括描述性统计、斯皮尔曼相关分析及卡方检验，同时也可用于依托主题分析法开展的定性编码流程。 所有数据均已按照伦理研究规范与经审批的院校流程完成完全匿名化处理。 本数据集旨在为高等教育场景下的人机交互、媒介素养及技术赋能学习相关研究的透明度提升、可重复性保障及后续拓展研究提供支持。