Humans need to accurately infer the intentions and feelings of others to engage in successful social interaction. However, the application of artificial intelligence technology in Education (AIEd) forms a human–machine collaborative environment which changed the interaction relationship of individuals, it may have an affect on them. This study aimed to explore whether AIEd affects adolescents’ emotional perception. Combined with the actual teaching situation and the result of the questionnaire, 1332 students recruited through random sampling from AI Curriculum Reform Demonstration Schools in Guangzhou participated in this study. Different emotional priming stimulative materials (sentences and situational pictures) were used in the experiments. The task was designed to investigate adolescents’ reaction time to emotional faces (positive, negative). After eliminating blank data and invalid data with response time greater than 150 ms, 977 and 962 valid data were included in the statistical analysis in experiment 1 and experiment 2 respectively. Results show that AIEd has a negative effect on adolescents’ emotional perception. Prior research has focused on theory to the exclusion of practical applications and the psychological impact of AIEd, thus this study makes an innovative contribution in exploring the impact of the application of artificial intelligence technology in education on adolescents’ physical and mental development by using empirical research methods.

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.

Evidently, the school climate is important in reducing adolescent problematic online game use (POGU); however, the mechanism accounting for this association remains largely unknown. This study examined whether loneliness mediated the link between school climate and adolescent POGU and whether this mediating process was moderated by adolescent intentional self-regulation. To this end, self-report questionnaires were distributed. Participants were 500 12–17-years-old Chinese adolescents (Mean_age = 13.59 years, 50.60% male). After controlling for adolescents' gender, age, family socioeconomic status, and self-esteem, the results showed that the negative association between school climate and adolescent POGU was partially mediated by loneliness. Moreover, this indirect link was stronger for adolescents with low intentional self-regulation than for those with high intentional self-regulation. These findings highlight loneliness as a potential mechanism linking school climate to adolescent POGU and provide guidance for the development of effective interventions for addressing the adverse effects of a negative school climate.

This study aimed to explore which factors had a greater impact on substance craving in people with substance use and the direction of the impact. A total of 895 male substance users completed questionnaires regarding substance craving, psychological security, positive psychological capital, interpersonal trust, alexithymia, impulsivity, parental conflict, aggression behavior, life events, family intimacy, and deviant peers. Calculating the factor importance by gradient boosting method (GBM), found that the psychosocial factors that had a greater impact on substance craving were, in order, life events, aggression behavior, positive psychological capital, interpersonal trust, psychological security, impulsivity, alexithymia, family intimacy, parental conflict, and deviant peers. Correlation analysis showed that life events, positive psychological capital, interpersonal trust, psychological security, and family intimacy negatively predicted substance craving, while aggression behavior, impulsivity, alexithymia, parental conflict, and deviant peers positively predicted substance cravings. These findings have important implications for the prevention and intervention of substance craving behavior among substance users.