Do learners exhibit a willingness to use ChatGPT? An advanced two-stage SEM-neural network approach for forecasting factors influencing ChatGPT adoption
{"title":"Do learners exhibit a willingness to use ChatGPT? An advanced two-stage SEM-neural network approach for forecasting factors influencing ChatGPT adoption","authors":"Nattaporn Thongsri, O. Tripak, Yukun Bao","doi":"10.1108/itse-01-2024-0001","DOIUrl":null,"url":null,"abstract":"Purpose\nThis 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.\n\nDesign/methodology/approach\nThis 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.\n\nFindings\nThe 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.\n\nPractical implications\nThe 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.\n\nOriginality/value\nThis 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.\n","PeriodicalId":44954,"journal":{"name":"Interactive Technology and Smart Education","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Interactive Technology and Smart Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/itse-01-2024-0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
期刊介绍:
Interactive Technology and Smart Education (ITSE) is a multi-disciplinary, peer-reviewed journal, which provides a distinct forum to specially promote innovation and participative research approaches. The following terms are defined, as used in the context of this journal: -Interactive Technology refers to all forms of digital technology, as described above, emphasizing innovation and human-/user-centred approaches. -Smart Education "SMART" is used as an acronym that refers to interactive technology that offers a more flexible and tailored approach to meet diverse individual requirements by being “Sensitive, Manageable, Adaptable, Responsive and Timely” to educators’ pedagogical strategies and learners’ educational and social needs’. -Articles are invited that explore innovative use of educational technologies that advance interactive technology in general and its applications in education in particular. The journal aims to bridge gaps in the field by promoting design research, action research, and continuous evaluation as an integral part of the development cycle of usable solutions/systems.