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.