Speaker
Description
This paper examines how students used artificial intelligence (AI) while writing professional bachelor theses and asks how those patterns can be described in a way that is useful for future support and governance in higher education. The study compared the 2024 graduating class and the 2025 graduating class from the same Lithuanian institution. The target population was 399 students, and the final sample included 258 respondents, with 129 from each graduating class. The questionnaire covered thesis-related tasks, named tools, perceived benefits and risks, attitudinal statements, and open comments. Quantitative data were analysed in SPSS using frequencies, percentages, reliability analysis, Shapiro-Wilk tests, non-parametric comparison logic, and Spearman correlations. Open comments were grouped thematically. The results show a stable pattern. Students used AI most often for paraphrasing and summarising, translation, grammar and style revision, and literature search. ChatGPT and Google Scholar clearly dominated the tool profile. Students valued AI mainly because it saved time and made writing easier to manage, but they also pointed to errors, weaker originality, and the need to verify AI-generated content. The strongest agreement concerned lecturer guidance on acceptable AI use. For an engineering audience, the paper offers a layered analytical model—context variables, task-use indicators, tool-use indicators, and governance indicators—that can serve as requirement-level evidence for future AI-assisted supervision or decision-support systems.