Digital Literacy, Reading Interest, and Generative AI Use in University Students' Learning Motivation (Jurnal MEKOM (Media Komunikasi Pendidikan Kejuruan), 2026)
Purpose – The rapid adoption of digital technologies and Generative AI (ChatGPT) has transformed learning practices in higher education, requiring students to develop strong digital competencies while maintaining academic reading habits. This study aims to examine the patterns of digital literacy, reading interest, and Generative AI (ChatGPT) use in relation to learning motivation among university students using descriptive statistics and cross-tabulation analysis. Methods – A descriptive quantitative approach was employed involving 306 students from the Department of Informatics and Computer Engineering, Universitas Negeri Makassar. Data were collected using a validated four-point Likert-scale questionnaire and analyzed through descriptive statistics and cross-tabulation analysis. Findings – Most students demonstrated high levels of digital literacy (94%), reading interest (79%), Generative AI (ChatGPT) use (92%), and learning motivation (92%). Cross-tabulation analysis revealed that the majority of respondents were concentrated in the high-category combinations across the variables. Research implications – These findings provide practical guidance for higher education institutions in designing learning strategies that strengthen digital literacy, cultivate academic reading habits, and encourage the ethical and responsible use of Generative AI (ChatGPT) to support students' learning motivation. Originality – This study contributes by providing a comprehensive descriptive profile of digital literacy, reading interest, and Generative AI (ChatGPT) use in relation to learning motivation through cross-tabulation analysis. Unlike previous studies that primarily focused on inferential relationships, this study highlights the distribution patterns among the variables, offering practical insights for technology-enhanced learning in higher education.