Disciplinary differences in undergraduate students' engagement with generative artificial intelligence

• Main Authors: Qu, Yao, Tan, Michelle Xin Yi, Wang, Jue
• Other Authors: School of Social Sciences
• Format: Article
• Language: English
• Published: 2025
• Subjects: Social Sciences; Generative artificial intelligence; Undergraduate students
• Online Access: https://hdl.handle.net/10356/182062
• Institution: Nanyang Technological University

The rapid development of generative artificial intelligence (GenAI) technologies has sparked widespread discussions about their potential applications in higher education. However, little is known about how students from various disciplines engage with GenAI tools. This study explores undergraduate students' GenAI knowledge, usage intentions, and task-specific engagement across academic disciplines. Using a disciplinary categorization framework, we examine how the hard/soft and pure/applied dimensions relate to students' interactions with GenAI. We surveyed 193 undergraduates from diverse disciplines at a university in Singapore. The questionnaire assessed students' GenAI knowledge, usage intentions, and engagement with GenAI for cognitive and routine tasks against their disciplinary background. The results indicate substantial disciplinary disparities in the level of engagement of students with GenAI. Compared to pure fields, applied fields (both hard and soft) consistently exhibit higher levels of GenAI knowledge and utilization intentions. Furthermore, the engagement of GenAI in routine tasks is relatively consistent across disciplines; however, there are substantial disparities in cognitive tasks, with applied fields exhibiting higher engagement. These results suggest that the practical orientation of applied fields drives GenAI adoption and utilization in academic settings. The study emphasizes considering disciplinary differences to better integrate GenAI into higher education and calls for tailored approaches that align with each field's unique epistemological and methodological traditions to balance GenAI's practical benefits with the preservation of core disciplinary knowledge and skills.