Performance, Interaction, and Ethical Evaluation in the Use of Generative Artificial Intelligence in Engineering Education: Anonymized and Aggregated Dataset

This dataset supports the manuscript Performance, Interaction, and Ethical Evaluation in the Use of Generative Artificial Intelligence in Engineering Education. The study examines engineering students’ engagement with generative artificial intelligence (GenAI) from two complementary perspectives: performance and interaction during a programming debugging task, and ethical evaluation of GenAI use in educational contexts. The repository is organized into two folders. phase1_debugging_task contains anonymized participant-level data from a classroom-based GenAI-assisted debugging activity with first-year engineering students. The dataset includes anonymized participant identifiers, task outcomes, number of prompts submitted to ChatGPT, data dictionary, and summary statistics. phase2_ethics_roleplay_survey contains aggregated data from an ethics role-play activity and post-activity survey with engineering students. The dataset includes case-level group decisions, aggregated Mentimeter voting results, ethical boundaries identified during the activity, and aggregated survey results on prior GenAI use, perceived usefulness, ethical concerns, institutional regulation, and perceived change in perspective after the activity. No directly identifying personal data are included. Phase 1 data are anonymized at participant level. Phase 2 data are provided only in aggregated form because of the small sample size and the exploratory nature of the qualitative activity. Individual open-ended responses are not included in the public dataset to reduce re-identification risk. The repository also includes supporting instruments, data dictionaries, summary statistics, and a README file describing the structure and reuse conditions of the dataset.