Use of Motives and Utilization of Artificial Intelligence as Predictors of Information Retention in Science

Abstract

The ongoing issue of poor information retention continues to hinder students' mastery of scientific concepts. This study examined the predictive power of the Use of Motives and Utilization of Artificial Intelligence on Information Retention in Science, as measured by students’ subjective assessment of their ability to recall and apply scientific concepts. The study was conducted among 266 Grade 12 STEM students in Davao City. Adopting a predictive research design, data were selected through total enumeration and rigorously analyzed using descriptive statistics, Pearson Product-moment correlation, and multiple linear regression analysis.
Descriptive results revealed that Use of Motives, Utilization of AI, and Information Retention all achieved "High" descriptive levels, suggesting that students possess strong internal drives and frequently engage with AI tools. Correlation analysis further indicated significant, moderately high positive relationships between both Use of Motives (r=0.674, p<0.05) and Utilization of AI (r=0.677, p<0.05) with the criterion variable. The multiple regression results demonstrated a significant combined predictive relationship, with the model (F=143.1, p<0.001) accounting for 52.1% of the total variance in perceived retention levels.
These findings partially confirm the Technology Acceptance Model (TAM), suggesting that students' cognitive success is significantly associated with the interplay between perceived usefulness and strategic digital engagement. The results are significant for school leaders and administrators in formulating technology-integrated policies to mitigate retention gaps. Furthermore, this study offers a foundational framework for future research to explore the remaining 47.9% of unexplained variance through qualitative factors or additional predictive variables.