Exploring The Use Of Generative AI In Planning, Content, And Evaluation Of Learning Among University Lecturers

This study examines the use of generative artificial intelligence (AI) among Indonesian university lecturers across three instructional domains: learning planning, content development, and assessment. It also investigates whether AI adoption levels differ across these domains and whether adoption intensity influences usage patterns. A quantitative within-subjects design was employed, involving 306 university lecturers with prior experience using generative AI in teaching. Data were collected through a five-point Likert-scale questionnaire and analyzed using descriptive statistics and repeated-measures analysis of variance (RM-ANOVA), following appropriate statistical assumption tests. The findings reveal relatively similar levels of AI use across the three instructional domains, with no statistically significant differences between them (p = 0.551). However, significant differences were identified between lecturers with high and low levels of AI adoption (p < 0.001), indicating consistently greater AI use among high-adoption lecturers across all domains. Furthermore, no significant interaction was found between instructional domains and adoption levels (p = 0.560). These findings suggest that the intensity of individual AI adoption plays a more prominent role in determining usage patterns than the specific instructional domain. The study highlights the importance of strengthening lecturers' AI competencies, particularly in prompt design, output verification, and ethical use, alongside institutional support to promote responsible and pedagogically meaningful AI integration in higher education.