Designing Teaching Materials with GenAI: Perceptions of Cognitive Load and Learner Competencies in Higher Education

Generative artificial intelligence (GenAI) is increasingly used to design instructional materials in higher education, yet limited evidence exists on students’ perceptions of the cognitive demands and learner-related competencies involved. Drawing on Cognitive Load Theory, this study examined university students’ perceptions of GenAI-supported instructional material design according to gender, age, and admission grade. A quantitative, non-experimental, ex post facto study was conducted with 451 university students enrolled in education-related undergraduate and master’s programmes. The instrument assessed perceived usefulness, beliefs concerning metacognitive practices, prior knowledge, and problem-solving strategies, and perceived intrinsic, extraneous, and germane cognitive load. Participants generally reported positive perceptions. Female students reported significantly higher scores on the metacognitive skills, prior knowledge, and problem-solving skills dimensions, although effect sizes were small. These differences should be interpreted cautiously, given that female students represented 82% of the sample. No statistically significant gender differences were detected in the cognitive load dimensions. Age showed only a weak negative association with extraneous cognitive load, whereas admission grade was positively associated with metacognitive scores in both gender groups and with prior knowledge and problem-solving scores among female students. The findings describe students’ beliefs about the practices and cognitive demands associated with GenAI-supported material design and may inform training in the critical review and pedagogical adaptation of AI-generated teaching materials.