Raw data of pre-service teachers' digital competence scale across three measurement points (T1–T3)

Dataset Summary This dataset was collected to empirically examine the effects of an Instructor–Student–AI Collaborative (ISAC) model on pre-service teachers' digital competence in the era of generative AI. The data were gathered over a 16-week semester at a Chinese normal university, following a convergent mixed-methods design. Nature of the Data The file contains longitudinal quantitative survey responses from 23 pre-service teachers. Participants completed the Pre-service Teacher Digital Competence Scale at three time points: pre-test (T1, Week 1), mid-test (T2, Week 8), and post-test (T3, Week 16). The scale comprises 23 Likert-scale items (1–5) measuring six dimensions: Digital Awareness, Digital Knowledge & Skills, Digital Application, Digital Social Responsibility, Digital Learning, and Digital Teaching Practice. Demographic variables include gender, grade, age, and intention to become a teacher. Scope and Usage The dataset enables repeated measures analyses to examine overall competence development and divergent trajectories between technical and ethical dimensions across time. It can also be linked to qualitative data (reflection journals, interaction logs, and interview transcripts) to explore the cognitive mechanisms underlying human–AI collaboration. The data are suitable for researchers interested in teacher education, AI in education, and digital competence development.