Search results

This study aims to examine the variables that influence learners’ acceptance of chat generative pre-trained transformer (ChatGPT) through the theoretical synthesis of variables in the field of behavioral science. It uses the use and gratifications theory in conjunction with variables related to the information system (IS), as proposed by the Delone and McLean IS success model.

This quantitative research collected data from 679 undergraduate students using stratified random sampling. A two-staged structural equation modeling (SEM)-neural network approach was used to analyze the data, with SEM used to study the factors influencing the intention to use ChatGPT. Additionally, an artificial neural network approach was used to confirm the results obtained through SEM.

The two-staged SEM-neural network approach yielded robust and consistent analysis results, indicating that the variable “System quality (SYQ)” has the highest influence, followed by “Cognitive need (CN),” “Information Quality (INQ),” “Social need (SN)” and “Affective need (AN)” in descending order of importance.

The results obtained from integrating the behavioral variables with IS variables will provide guidance to various organizations, such as the Ministry of Education, universities and educators, in the application of artificial intelligence technology in learning. They should prioritize the quality aspect of the system and the technological infrastructure that supports the use of ChatGPT for learning. Additionally, they should prepare learners to be ready in various dimensions, including knowledge, emotions and social aspects.

This study presents challenges in implementing artificial intelligence technology in learning, which educational institutions must embrace to keep up with the global technological trends. The educational sector should integrate artificial intelligence into the curriculum planning, teaching methods and learner assessment processes from the outset.