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 (Mean_age = 10.6), 351 junior high school students (Mean_age = 13.1), and 639 senior high school students (Mean_age = 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.

Whether face perception involves domain-specific or domain-general processing is an extensively debated issue. Relative to non-face objects and alphabetical scripts, Chinese characters provide a good contrast to faces because of their structural configuration, requirement for high level of visual expertise to literate Chinese people, and unique appearance and identity for each individual stimulus. To examine potential categorical differences in their neural processing, event-related potentials (ERPs) were recorded to blocked face and Chinese character stimuli. Fast adaptation method was applied to better control for the low-level stimulus difference between faces and Chinese characters. Participants were required to respond to the color of the outer frame in which these stimuli were presented, at either a fast (ISI 650 ms) or slow (ISI 1300 ms) rate, and with an orientation that was either the same or alternated between upright and inverted. Faces elicited a larger and later N170 relative to characters, but the N170 was more left-lateralized for characters relative to the faces. Adaptation-by-rate and adaptation-by-orientation effects were observed on the amplitude of N170, and both were more pronounced for faces relative to characters. Inverted stimuli elicited a later N170 relative to upright stimuli, without amplitude change, and this inversion effect was more pronounced for faces relative to characters. Moreover, faces elicited a larger and later P1 and a larger adaptation-by-rate effect on P1 relative to characters. The adaptation-by-orientation effect was illustrated by a larger P1 under the same relative to the alternated orientation condition. Therefore, evidence from the amplitude and the lateralization of N170, the stimulus inversion effect on N170 latency, and the neural adaptation between faces and Chinese characters on P1 and N170 components support the notion that the processing of faces and Chinese characters involve categorically different neural mechanisms.

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.

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. In this study, we propose and validate a novel implicit measure to predict an individual's levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Participants viewed Chinese words with positive, negative, and neutral emotions. The multi-channel event-related potentials elicited by these emotional words were used to train a sparse regression model for personality prediction. Results from a large test sample of 196 participants indicated that the personality scores derived from the proposed measure reached significant correlations with a commonly used questionnaire (r = .50, .60, .49, .55, and .49 for agreeableness, conscientiousness, neuroticism, openness, and extraversion, respectively). The EEG-based personality scores showed good external validity as well, capable of predicting behavioral indices and psychological adjustment similar to self-reported scores. Besides, the EEG-based scores were relatively stable across time, as reflected by the test-retest reliability of .5 ~ .7 for the five personality traits within a cohort of 33 participants 19-78 days later. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.