Dataset for: ChatGPT in Academic Learning: Linking Informational Motives to Student Satisfaction

This dataset was compiled for a research study entitled “ChatGPT in Academic Learning: Linking Informational Motives to Student Satisfaction.” This study was motivated by the problem that academic materials in higher education often contain complex concepts, technical terms, or jargon, creating semantic noise among students that causes them to misunderstand academic messages. To prevent this noise, students utilize AI-based communication media, such as ChatGPT, to seek information that can support their academic understanding. Referring to the Uses and Gratifications framework, this study examines: the strength of students’ informational motives in using ChatGPT to understand academic messages, their level of satisfaction with the academic understanding obtained through the use of ChatGPT, and the relationship between such motives and satisfaction. Accordingly, data were collected through the distribution of questionnaires to 100 students from the 2022 cohort at a private university in Indonesia who had used ChatGPT to understand academic messages. All data were collected anonymously and are provided for research transparency and verification purposes. This dataset contains anonymized survey responses from university students. The repository includes raw respondent data in Excel format, SPSS output files for validity, reliability, descriptive statistics, normality, linearity, correlation, and regression analyses, a PDF summary of the statistical results, and a PDF file containing the questionnaire items. The questionnaire was used to measure two variables: informational motives in using ChatGPT to understand academic messages (X) and students' satisfaction with their understanding of academic messages (Y). The questionnaire consists of 12 items, and all items were measured using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). Six items asked respondents to respond to statements related to informational motives in using ChatGPT (X) with various information-need indicators as follows: X1 – “Simple explanations” X2 – “Definitions of technical terms” X3 – “Summaries of materials” X4 – “Practical examples” X5 – “Additional learning resources” X6 – “Rapid comprehension of academic information” The other six items measured students' satisfaction with their understanding of academic messages (Y) using various indicators related to media-use experiences as follows: Y1 – “Clarity of language” Y2 – “Coherence of information flow” Y3 – “Relevance of responses” Y4 – “Response timeliness” Y5 – “Perceived understanding accuracy” Y6 – “Perceived usefulness of information” The analyses conducted include validity and reliability tests to ensure that all questionnaire items are valid and consistent, descriptive statistical analysis to determine the mean and standard deviation of all items (to identify the levels of informational motives and satisfaction), normality and linearity tests as prerequisites for Pearson correlation analysis, correlation analysis to examine the relationship between variables, and regression analysis to determine the effect of variable X on Y, including the direction, significance, and magnitude of the effect.