The application of artificial intelligence technology in education influences Chinese adolescent's emotional perception.

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.

Psychosocial Factors Predict the Level of Aggression of People with Drug Addiction: A Machine Learning Approach.

This study aimed to identify the relevant psychosocial factors that can predict the aggression in people with drug addiction. A total of 896 male participants (Meanage = 38.30 years) completed the survey. Gradient boosting regression, a machine learning algorithm, was used to find the relevant psychosocial variables, such as psychological security, psychological capital, interpersonal trust and alexithymia, that may be significantly related to aggressive behavior. Results showed that the five most important factors in the prediction of aggression are interpersonal trust, psychological security, psychological capital, parental conflict and alexithymia. A high level of interpersonal trust, psychological security and psychological capital can predict a low level of aggression in people with drug addiction, while a high level of parental conflict and alexithymia can predict a high level of aggression. Overall, the findings highlight the need to focus interventions according to the relation between these psychosocial factors and aggression in order to decrease violence.

'Can we predict aggression?'-Determining the predictors of aggression among individuals with substance use disorder in China undergoing enforced detoxification through machine learning.

BACKGROUND: The general aggression model has shown that both individual and situational factors can predict aggression. However, past research has tended to discuss these two factors separately, which might lead to inconsistency. This study addresses this gap by examining the importance of each predictor of aggression in a Chinese compulsory drug treatment population and further explores the predictors of aggression in various substance use disorder populations. METHOD: Analyses were conducted using a sample of 894 male participants (mean = 38.30, SD = 8.38) in Chinese compulsory drug rehab. A machine learning model named LightGBM was employed to make predictions. We then used a game-theoretic explanatory technique, SHAP, to estimate the effect of predictors. RESULTS: In the full-sample model, psychological security, parental conflict, and impulsivity were the top 3 predictors. Depression, childhood abuse, and alexithymia positively predicted aggression, whereas psychological security, family cohesion, and gratitude negatively predicted aggression. There were significant differences in the predictive effects of depressants and stimulants. Although the importance of predictors varied between drug-use groups, several individual and situational factors were consistently the most important predictors. LIMITATIONS: All participants in this study were male, and the data were acquired through self-reports from the participants. Domestic and nondomestic aggression are not distinguished. Additionally, our findings cannot support causal conclusions. CONCLUSION: This study tested a series of classical theories of the predictors of aggression in China's compulsory drug treatment context and extended the ideas of the GAM to various substance use disorder groups. The findings have important implications for aggression treatment.

Influence of artificial intelligence in education on adolescents' social adaptability: The mediatory role of social support.

Artificial intelligence (AI) is widely used in the field of education at present, but people know little about its possible impacts, especially on the physical and mental development of the educated. It is important to explore the possible impacts of the application of artificial intelligence in education (AIEd) in order to avoid the possible adverse effects. Prior research has focused on theory to the exclusion of the psychological impact of AIEd, and the empirical research was relatively lacking. This study aimed to identify the influence of AIEd on adolescents' social adaptability via social support. A total of 1332 students were recruited using random sampling from 13 Artificial Intelligence Curriculum Reform Experimental Schools in Guangzhou, Southern China, completed the survey. There were 342 primary school students (Meanage = 10.6), 351 junior high school students (Meanage = 13.1), and 639 senior high school students (Meanage = 15.8). Results showed that AIEd has a negative impact on adolescents' social adaptability, and is significantly negatively correlated with social adaptability and family support, but there is no significant correlation with school support. AIEd could not only affect social adaptability directly, but also could affected it through the family support.

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

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 (meanage = 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.

Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach.

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.

Interparental Conflict and Delinquency Among Chinese Adolescents: Parental Knowledge as a Mediator and Deviant Peer Affiliation as a Moderator.

Interparental conflict has been found to positively affect adolescent delinquency; however, the underlying mechanism that explains this association remains unclear. This study investigated whether parental knowledge mediates the association between interparental conflict and adolescent delinquency, and whether this mediating process is moderated by deviant peer affiliation. To examine this, a total of 3,129 Chinese adolescents (47.27% boys, Mean age = 14.94 years) completed a survey. Structural equation modeling indicated that the positive association between interparental conflict and adolescent delinquency is mediated by parental knowledge. Moreover, for adolescents with high deviant peer affiliation, interparental conflict was found to positively predict delinquency via parental knowledge; however, this indirect link was non-significant for adolescents with low deviant peer affiliation. These findings highlight the influence of parental knowledge and deviant peer affiliation on the association between interparental conflict and adolescent delinquency. This can provide guidance for the development of effective interventions that address the adverse effects of interparental conflict.