Dataset of AI Adoption Usage, Expectation, Attitudes, Perceptions, and Motivations for Learning in Higher Education

Description

This dataset captures insights into the use of Artificial Intelligence (AI) among 535 students in Indonesian higher education, focusing on their expectations, challenges, attitudes, perceptions, and motivations regarding AI-based learning tools. Collected through a structured survey, the dataset includes demographic variables such as university type, field of study, and educational level, along with students' self-reported experiences with AI in academic settings. The dataset serves as a valuable resource for understanding AI adoption trends in higher education, identifying barriers to AI integration, and evaluating its impact on student engagement and learning outcomes. It enables comparative analysis across different academic disciplines and institutional contexts, offering opportunities for policymakers and educators to design AI-informed curricula. Additionally, this dataset is structured for reproducibility and reuse, allowing researchers to extend its findings, apply alternative analytical approaches, and conduct cross-regional or longitudinal studies on AI integration in higher education.

Steps to reproduce

The dataset was generated by distributing an online questionnaire via Google Forms to respondents, capturing their learning experiences influenced by AI usage. This method was chosen to facilitate broad respondent reach through simple random sampling. The collected responses were stored in an Excel file, systematically coded, and imported into IBM SPSS Statistics version 29 for analysis. Data normalization was assessed by ensuring the consistency of standardized response scores. Descriptive statistical analysis was conducted, including calculations of mean, standard deviation, frequency distribution, and reliability scores evaluated using Cronbach’s alpha to verify the internal consistency of survey constructs. Additionally, quantitative analysis using SPSS explored relationships between demographic characteristics and AI usage variables, including Performance Expectations, Challenge Using AI, Attitudes Toward Using AI, Perception of Using AI, and Motivation Intention to Use AI. The correlation matrix in Table 3 illustrates the strength and direction of these relationships, while Table 4 presents ANOVA results to assess significant differences among groups. Finally, Table 5 provides regression analysis findings, demonstrating how demographic characteristics predict AI usage constructs, particularly in relation to students' academic experiences and task completion.

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