The Impact of Generative AI on University Students’ Academic Performance: A Quantitative and Predictive Analysis - Sazeed Hossain; Shahria Zzaman; Afsana Mimi (Journal of Technology Informatics and Engineering, 2026)
This research is concerned with assessing the effects of generative AI on the academic performance of university students in terms of usage, benefits, and challenges. Quantitative research methodology was used for data collection via structured questionnaires from 256 university students. Data analysis was performed through IBM SPSS (version 27.0) and Stata software utilizing non-parametric statistics considering the type of data. The reliability test showed that the instrument was highly reliable with a Cronbach's Alpha score of 0.842. The results show a statistically significant positive association between the different variables of AI use, with research assistance being the most significant predictor of academic performance. Exploratory factor analysis indicated three important factors affecting academic performance, namely academic integration, perception of challenges, and skill development. Regression analysis showed a positive relationship between academic integration and skill development and academic performance. However, perception of challenges had a negative relationship. In addition to this, another strong relationship between the geographical position of students and awareness about risks related to generative AI was observed. In general, the research offers a balanced look at the role of generative AI in academia as a very helpful technology, as well as a risky one, which has to be approached thoughtfully.