Data and Code for "AI-Assisted Interactive Narrative as an Integrative-Learning Design Task: Student Appraisal in Preservice Teacher Education"

This data-and-code package supports the article “AI-Assisted Interactive Narrative as an Integrative-Learning Design Task: Student Appraisal in Preservice Teacher Education” published in Thinking Skills and Creativity (https://doi.org/10.1016/j.tsc.2026.102366). The quantitative materials cover three independent cohorts totaling N = 1,064: a December 2024 primary cohort (N = 480), an independent December 2025 shortened-process cohort (n = 327), and an independent June–July 2026 subsequent same-course cohort (n = 257). A separate anonymous June 2025 different-course follow-up contains 317 response records from the same Year-2022 class population represented in the primary cohort. These records are non-additive, cannot be linked to the December 2024 records at the individual level, and are used only for unmatched descriptive comparison. The package supports descriptive summaries, dimension-reduction checks, project-cluster-robust regression, ordinal WLSMV analyses, estimator triangulation, sparse-category diagnostics, and equivalent-model sensitivity analyses. The qualitative materials include a post hoc reconstructed 203-record primary working corpus, a separate retained 229-response June 2025 working corpus, a 12-theme codebook, two frozen human-coder matrices, and human-only agreement statistics. Final descriptive frequencies retain the shared human code when the coders agreed and use the corresponding author’s adopted AI-assisted resolution when they disagreed; item-level records and rationales are provided for all 607 disagreements. The original unfiltered June 2025 export and contemporaneous documentation of its retention rule are unavailable. Identifiable platform exports, student identities, and identifiable student-project materials are excluded. README files and SHA-256 manifests document execution, provenance, file integrity, and evidentiary boundaries. These materials support reproducible analyses of task appraisal and implementation conditions; they do not establish causal effects, objective learning, creativity, internalization, transfer, paired cross-course change, or independent qualitative replication.