Influence of Artificial Intelligence in Education on Adolescents’ Social Adaptability: A Machine Learning Study

Chuyin Xie, Minhua Ruan, Ping Lin, Zheng Wang, Tinghong Lai, Ying Xie, Shimin Fu and Hong Lu
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Chuyin Xie: School of Education, Guangzhou University, Guangzhou 510006, China
Minhua Ruan: School of Education, Guangzhou University, Guangzhou 510006, China
Ping Lin: School of Education, Guangzhou University, Guangzhou 510006, China
Zheng Wang: Management Center for Quality Education of Baiyun District, Guangzhou 510080, China
Tinghong Lai: School of Education, Guangzhou University, Guangzhou 510006, China
Ying Xie: School of Public Administration, Guangzhou University, Guangzhou 510006, China
Shimin Fu: School of Education, Guangzhou University, Guangzhou 510006, China
Hong Lu: School of Education, Guangzhou University, Guangzhou 510006, China

IJERPH, 2022, vol. 19, issue 13, 1-12

Abstract: This study aimed to investigate the influence of artificial intelligence in education (AIEd) on adolescents’ social adaptability, as well as to identify the relevant psychosocial factors that can predict adolescents’ social adaptability. A total of 1328 participants (mean age = 13.89 , SD = 2.22 ) completed the survey. A machine-learning algorithm was used to find out whether AIEd may influence adolescents’ social adaptability as well as the relevant psychosocial variables, such as teacher–student relations, peer relations, interparental relations, and loneliness that may be significantly related to social adaptability. Results showed that it has a positive influence of AIEd on adolescents’ social adaptability. In addition, the four most important factors in the prediction of social adaptability among AI group students are interpersonal relationships, peer relations, academic emotion, and loneliness. A high level of interpersonal relationships and peer relations can predict a high level of social adaptability among the AI group students, while a high level of academic emotion and loneliness can predict a low level of social adaptability. Overall, the findings highlight the need to focus interventions according to the relation between these psychosocial factors and social adaptability in order to increase the positive influence of AIEd and promote the development of social adaptability.

Keywords: artificial intelligence in education; adolescent; social adaptability; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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