AI usage and history students
Description
The dataset was designed to explore the factors that most significantly influence the level of AI tool usage among history students at Jan Evangelista Purkyně University (UJEP) in their academic work. It directly addresses three core research questions: - RQ1 investigates which perceptions, attitudes, and behavioral intentions shape AI tool usage. - RQ2 examines the antecedent factors—such as demographic characteristics, digital literacy, and epistemic orientation—that may predict AI engagement. - RQ3 probes whether frequent AI tool users represent a distinct subgroup, suggesting non-random selection into higher usage patterns. The central variable, "AIFrequency", captures students’ self-reported frequency of AI tool use in study-related tasks. This is an ordinal variable measured on a 5-point Likert scale, ranging from 1 ("Never") to 5 ("Daily or Almost Daily"). This variable serves as the primary outcome measure, enabling analysis of usage intensity across different student profiles. The survey also includes items aligned with Technology Acceptance Models (TAM and VAM), covering constructs such as perceived usefulness, ease of use, trust, and intention to continue using AI tools. These constructs are operationalized through multiple Likert-scale items, allowing for both descriptive and inferential statistical analysis. The dataset thus provides a rich foundation for examining not only how often students use AI tools, but also why they do so, and who is most likely to engage with them regularly.
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Steps to reproduce
We investigated student experiences with AI tools in academic settings, focusing on history students at Jan Evangelista Purkyně University in Ústí nad Labem (UJEP). The survey (see variable description file) was designed entirely from our theoretical framework, without adapting prior instruments, and followed the principles of Technology Acceptance Models (TAM and VAM). History students were chosen as a distinct population due to their presumed digital literacy and their critical epistemic culture, making them ideal for examining AI adoption challenges. Using convenience sampling with stratification by academic year, age, and gender, we targeted the “INTRODUCTION INTO HISTORY STUDIES” course, which had 164 enrolled students. A pilot study involving 21 students and 3 AI experts helped refine the survey for clarity and precision. Data were collected via Google Forms and distributed through official university channels and QR codes during lectures. Out of 125 responses received (a 73.17% response rate), 120 were deemed valid after excluding incomplete submissions.