Patterns and Extent of Generative AI Use Among College Students: A Demographic-Based Quantitative Analysis - Neil J. Pelino; Junry P. Bacalso; Jellow S. Painagan; Jennie Rose J. Dapar (Future Science / FSH-PH Publications, 2026)
This research investigated the trends and levels of generative artificial intelligence (AI) application among the students of the Bachelor of Science in Information Systems (BSIS) at Carmen Municipal College in the Academic Year 2025 to offer a localized approach to a Philippine-based context of higher education. Data was gathered aligned with descriptive quantitative design, 389 respondents (72.6% response rate) were surveyed using a standardized questionnaire and analysed with descriptive statistics and non-parametric tests. It has been found that the most common types of tasks by which students use generative AI are academically related and efficiency-based, namely, completing homework, brainstorming, seeking advice, and brainstorming, with an average usage of 3.15, meaning that AI is a facilitating learning tool, not an alternative to learning on their own. Inferential statistics showed that the difference in perceived AI influence was statistically significant among genders (U = 16,654, p =.047), year level (H (2) =11.40, p =.003), and age (H (4) = 9.95, p =.041), which means that perceived AI influence is different among the demographic groups in the institution. In contrast to the previous research that tends to generalize the application of AI by students in a larger context, the given study notes the patterns of its usage as conditioned by academic level and demographics of learners in a particular institutional environment. The findings can be used as context-specific information that can be used to develop evidence-based policies, AI literacy-focused programs, and responsible integration strategies in other similar public institutions of higher education.