Generative artificial intelligence and academic outcomes of Philippine information technology students

Abstract

Generative artificial intelligence (AI) is increasingly embedded in higher education, yet evidence from Philippine computing programs remains limited. This study examined whether student characteristics and generative AI use patterns are associated with academic outcomes among Bachelor of Science in Information Technology (BSIT) undergraduates at a Philippine public university. Using a quantitative-dominant mixed-methods design, the study administered a descriptive–correlational survey to 60 randomly selected students and used short interviews and open-ended responses to contextualize the quantitative results. Descriptive statistics summarized participant characteristics and tool use, while multivariate tests examined associations with perceived academic benefits and self-reported academic performance. Most respondents reported frequent use and high familiarity and described benefits for engagement, idea development, and learning flexibility. However, students also reported distraction, overreliance, and occasional inaccuracies. Age, frequency of use, and familiarity were significantly associated with academic outcomes, whereas gender and year level were not. The findings indicate that generative AI can support student learning when institutions pair curricular integration with verification practices, ethical-use guidance, and assessment designs that preserve independent thinking.