AI-Supported Quantitative Data Analysis in Foreign Language Research – Package is a comprehensive set of free teaching and learning materials designed to accompany AI-Supported Quantitative Data Analysis in Foreign Language Research.
The package provides a structured 15-week course framework for teaching AI-supported quantitative data analysis in foreign language education, ELT, applied linguistics, and related fields. It is suitable for university instructors, undergraduate and graduate students, doctoral researchers, teacher educators, and independent researchers.
The package includes 15 weekly presentations, a detailed Course Syllabus and Curriculum Guide, and an Instructor Guide. The presentations follow the analytical progression of the book, beginning with quantitative analytical reasoning, data-file preparation, documentation, variable transformation, and descriptive analysis, and continuing through internal consistency, exploratory factor analysis, homogeneity of variance, regression, correlation, t-tests, analysis of variance, repeated-measures designs, nonparametric tests, and data visualization.
The final presentation, Sample Prompts, brings the course together by presenting one sample prompt and corresponding example output from each section of the book. The prompts and outputs are drawn directly from the main text and demonstrate how AI-supported analytical tasks can be clearly defined, constrained, verified, and interpreted.
A distinctive feature of the course is its emphasis on authentic research evidence. The analytical examples are based on real datasets from published studies in foreign language education. AI-supported calculations, transformations, interpretations, tables, and visualizations are systematically checked against original datasets, study documentation, previously reported results, or independently reproduced calculations where appropriate.
The central principle of the course is: AI supports the analysis; the researcher retains methodological authority.
The materials emphasize methodological reasoning before statistical execution, preservation of original evidence, transparent documentation, verification of consequential AI-generated output, careful interpretation of statistical results, and researcher responsibility for final analytical decisions and scientific claims.
The package is intended to support, not replace, the book. The book provides the full explanations, analytical examples, prompts, tables, figures, methodological discussions, and verification procedures, while the course resources provide an organized instructional framework for using that content across a 15-week course.
All course resources are available free of charge on LREplus.
