Data and code for: Investigating the Washback Effect on Generative AI

This repository holds the files used in the paper Investigating the Washback Effect on Generative AI Strategies: Insights from Engineering Students.<br><br>The dataset comes from a mixed-methods study of how first- and second-year engineering students use generative AI (e.g. ChatGPT) as a study aid in relation to course assessment and perceived relevance. It contains anonymised survey responses from 336 students in mandatory foundation courses (mathematics, physics, programming, chemistry), including Likert-scale measures of procedural vs. conceptual GenAI use, perceived assessment criteria (procedural vs. conceptual), perceived course relevance, and basic background variables. These data were used to estimate a psychometric measurement model (EFA/CFA) and a structural equation model testing how assessment orientation and relevance predict different types of GenAI use, supplemented by 11 qualitative interviews (not included in this repository).<br><br>The Rmd file used for data analysis, figure and table production is available in the folder "Data and Code" along with the raw survey data in the file "complete.csv"<br>The file Survey.pdf shows the exact questionnaire used for data collection<br><br>Additionally, the folder "Summary report" contains an HTML file generated by Quarto as well as supplementary files to run it. The HTML shows all relevant graphs and statistics for the paper.<br><br><b>Various licenses apply to the files in this item:</b>complete.csv: CC-BY 4.0Washback_data_analysis.Rmd: MIT License Copyright © 2025 Technical University of DenmarkAll files in the folder "Summary report": MIT License Copyright © 2025 Technical University of DenmarkSurvey.pdf: CC-BY 4.0readme.txt: CC-BY 4.0<br>