Artificial Intelligence in Higher Education: A Transdisciplinary Analysis of Students’ Course Understanding and Perceived Academic Performance
This study investigates the relationship between students’ use of Artificial Intelligence (AI) in higher education, their understanding of the course content and their perceived academic performance. Using the transdisciplinary framework of Basarab Nicolescu and the Informing Science theory, we analysed survey data collected from 200 university students. A Chi-Square Test of Independence ($\chi^2(1, N=200) = 50.51, p < .001, \phi = .50$) shows that students who use AI for cognitive reasons, such as clarifying complex concepts, are almost three times more likely to report a deep understanding of their coursework than students who use it mechanically to generate text fast. Crucially, the data reveal a cognitive paradox: this increased understanding does not result in higher formal grades. These findings suggest that universities need to abandon a punitive compliance regime and reform traditional assessment models to better evaluate and support symbiotic human-AI learning.