Correlation: CICI AI Utilization and General Weighted Average in Gen Z Stem Students

Robert Bernales and Angelica S. Fortuna
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Angelica S. Fortuna: Handumanan National High School, Bacolod City, Negros Occcidental, Philippines

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 12, 655-665

Abstract: This study investigated the correlation between the utilization of the CICI AI application and the general weighted average (GWA) of Grade 11 Generation Z (Gen Z) STEM students in a Department of Education Senior High School in Bacolod City during the 1st Quarter of the School Year 2023-2024. The research aimed to determine the demographic profile of the students, the level of CICI AI utilization, differences in utilization based on demographics, and the relationship between utilization and academic performance. The research design was described as descriptive-correlational, employing quantitative methods for data collection and analysis. Data were gathered through a self-made questionnaire distributed online, and statistical tools such as frequency and percentage, mean and standard deviation, t-test, ANOVA, and Pearson coefficient were used for analysis. The findings indicated high utilization levels of the CICI AI application across all aspects, with no significant differences based on demographics such as sex, age, socio-economic status, or academic achievement. Moreover, no significant relationship was found between CICI AI utilization and students’ GWA. Statistical analysis reinforced these results, demonstrating that variations in academic performance are likely influenced by other factors beyond AI usage. The study acknowledged the multifaceted nature of academic success, highlighting that it involves various elements. Recommendations included integrating AI tools into STEM education to complement existing teaching strategies, adopting innovative methodologies to enhance engagement, and fostering responsible AI usage among students to maximize its benefits. Future research was suggested to explore additional factors such as motivation, study habits, and classroom environment that influence academic performance, as well as to investigate the legal and ethical implications of AI integration in education.

Date: 2024
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