This study aimed to explore the current situation regarding medical students’ professional identity after COVID-19 in China, as well as the factors that influence it. Questionnaire compiled by Fujian Medical University and self-designed were used, and participators were from one Medical University in Jiangxi Province, a central city of China. Results showed that the professional identity of medical students was upper middle level and the professional attitude was generally positive. There was a significant sex difference in terms of value (t = 2.057, p < 0.05) which the scores for boys were higher than girls, whereas the scores for girls were higher than boys when it came to aspects such as professional environment (t = -3.918, p < 0.001) and professional cognition (t = -3.855, p < 0.001). There was a significant difference in the sense of professional identity between people with and without siblings (t = 2.264, p < 0.05). The scores of students who participated in prevention and control of the epidemic were significantly higher than those who did not (t = 2.267, p < 0.01). Professional identity decreased gradually in related to higher grades, but it increased at the graduate stage; the grade [F (5,635) = 10.302, p < 0.001] and majors [F(2,635) = 5.718, p < 0.01] differences were significant. Factors such as family members’ influence, attitude toward occupation, grades, major, registered residence, and college choosing were the main factors that influenced medical students’ professional identity. Overall, the medical students’ professional identity needs to be further strengthened in the post COVID-19, it should be increased education regarding career development and planning.

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.

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.