Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool

Patrick M Cortez; Ardvin Kester S Ong; John Francis T Diaz; Josephine D German; Singh Jassel Satwant Singh Jagdeep

doi:10.1016/j.heliyon.2024.e25896

Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool

Heliyon. 2024 Feb 6;10(3):e25896. doi: 10.1016/j.heliyon.2024.e25896. eCollection 2024 Feb 15.

Authors

Patrick M Cortez¹, Ardvin Kester S Ong^{1

2}, John Francis T Diaz³, Josephine D German¹, Singh Jassel Satwant Singh Jagdeep²

Affiliations

¹ School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila, 1002, Philippines.
² E.T. Yuchengo School of Business, Mapúa University, 1191 Pablo Ocampo Sr. Ext., Makati, Metro Manila 1205, Philippines.
³ Department of Finance and Accounting, Asian Institute of Management, 123 Paseo de Roxas, Legazpi Village, Makati, 1229, Metro Manila, Philippines.

Abstract

During the pandemic, artificial intelligence was employed and utilized by students around the globe. Students' conduct changed in a variety of ways when schooling returned to regular instruction. This study aimed to analyze the student's behavioral intention and actual academic use of communicational AI (CAI) as an educational tool. This study identified the variables by utilizing an integrated framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and self-determination theory. Through the use of an online survey and Structural Equation Modeling, data from 533 respondents were analyzed. The results showed that perceived relatedness has the most significant effect on the behavioral intention of students in using CAI as an educational tool, followed by perceived autonomy. It showed that students use CAI based on the objective and the possibility of increasing their productivity, rather than any other purpose in the education setting. Among the UTAUT2 domains, only facilitating conditions, habit, and performance expectancy provided a significant direct effect on behavioral intention and an indirect effect on actual academic use. Further implications were presented. Moreover, the methodology and framework of this study could be extended and applied to educational technology-related studies. Lastly, the outcome of this study may be considered in analyzing the behavioral intention of the students as the teaching-learning environment is still continuously expanding and developing.

Keywords: Communicational artificial intelligence; Education; Self-determination theory; Structural equation modeling; Unified theory of acceptance and use of technology.