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

Purpose: This dataset was collected to evaluate the effects of the Instructor–Student–AI Collaborative (ISAC) model on pre-service teachers' digital competence development over a 16-week intervention. It serves the quantitative strand of a convergent mixed-methods study, enabling repeated measures analyses of six digital competence dimensions and identification of non-linear developmental trajectories—particularly the divergence between technical skill acquisition and ethical awareness. Nature: Structured, longitudinal quantitative data in Excel format (.xlsx). Contains 23 pre-service teachers' item-level responses (1–5 Likert scale) on the Pre-service Teacher Digital Competence Scale, administered at three time points: pre-test (T1, Week 1), mid-test (T2, Week 8), and post-test (T3, Week 16). All data are pseudonymized. The scale comprises 23 items mapping to six dimensions: Digital Awareness (2 items), Digital Knowledge & Skills (3 items), Digital Application (5 items), Digital Social Responsibility (3 items), Digital Learning (4 items), and Digital Teaching Practice (6 items). No responses are missing; the dataset is complete. Scope: N = 23 pre-service teachers from an intact class at a Chinese normal university. Demographic variables include gender, age (20–21 years), grade, and intention to pursue a teaching career. Data span Week 1 through Week 16 of the intervention. Each row represents one participant; columns represent demographic variables followed by item scores for T1, T2, and T3. Data collection: Administered online via Wenjuanxing survey platform under institutional ethical approval. Raw item scores are preserved without transformation or aggregation. Usage and citation: When reusing this dataset, please cite the associated article: \"Enhancing Digital Competence in Pre-service Teachers: A Mixed-Methods Evaluation of the Instructor-Student-AI Collaborative Model\".