Investigating the Acceptability of Students on Generative AI Tools: A Correlation Analysis among Selected Tertiary Schools in Metro Manila, Philippines

Angelo C. Arguson; Rhonnel S. Paculanan; Marvin P. Mabborang

doi:10.55529/jaimlnn.45.7.18

Authors

Angelo C. Arguson Computer Science, FEU Institute of Technology, Manila, Philippines.
Rhonnel S. Paculanan School of Information Technology, Arellano University, Manila, Philippines
Marvin P. Mabborang Administrative Services, DepEd Manila, Philippines.

Keywords:

Correlation, Generative AI, Academic Requirements, Acceptability Study.

Abstract

Artificial Intelligence has become the emphasis on the needs of future society. The digital era of education demands critical thinking skills, digital literacy and fundamental abilities to navigate and verify information. This development can be supported by the ability of AI chatbots to provide large volume of information in an interactive and efficient way. The goal of this paper is to examine the interest of the chosen BSCS and BSIT students in Metro Manila in utilizing three generative AI tools: Copilot, ChatGPT, and Gemini in the accomplishment of their academic requirements. A one-shot case study was carried out by the authors on six (6) selected universities in Metro Manila. A voluntary response sampling approach was employed to gather the participants in this study. The Slovin's formula was used to calculate statistically enough samples from the population. This study involved 209 CS and IT students, 157 or 75.12% of whom were males and 52 or 24.88% of whom were females, 45 or 21.53% were BSCS students and 164 or 78.47% were BSIT students. The data was initially recorded in a comma-separated value file. The extended TAM instrument was facilitated online using Google Forms by the participants which then imported into SPSS for data understanding and statistical treatment. It was found that out of eighteen (18) observed inter-item relationships, only six (6) or 33.33% were statistically significant and had considerable impacts. Moreover, SN → AT has the highest coefficient value of 0.788 followed by the factor PU → AT of 0.732 indicating their influence on generative AI tools attitude of use (AT). Lastly, PE → AT was shown having the lowest among relationships with 0.533 coefficient value. The study concluded that computer tertiary students in Metro Manila were generally amenable to the idea of introducing generative AI techniques into their academic responsibilities.