The past two decades have witnessed controversy over whether the use of digital technology has damaged or enhanced adolescents’ social relationships, which influences their development. In this study, we addressed this debate by specifying the effect of different types of smartphone use content on social relationships, rather than simply relying on screen time spent on digital media. To avoid selective analysis and report of different variables, we used specification curve analysis (SCA) in a large dataset (N = 46,018) to explore the correlations between 20 types of smartphone use content and adolescents’ social relationships (parent–child, peer, and teacher–student). The types of smartphone use content were measured by the revised version of Mobile Phone Use Pattern Scale, the Parent-Child Relationship Scale, the Peer Relationship Scale, and the Teacher-Student Relationship Scale assessed three different social relationships, respectively. Of the 20 types of smartphone use content, only playing games (negatively explaining 1% of the variation), taking online courses (positively explaining 1.6% of the variation), using search engines (positively explaining 1.2% of the variation), using a dictionary (positively explaining 1.3% of the variation), and obtaining life information (positively explaining 1.5% of the variation) showed a significant effect size. The association between smartphone use and adolescents’ social relationships depends on the various types of content with which adolescents engage during smartphone use. The various effects of different types of smartphone use content deserve the attention of both the public and policy-makers.

Although there are theoretically expected associations between problematic smartphone use (PSU) and materialism, there is a lack of research that examines these associations using a longitudinal design, focusing on both within-person and between-person effects. Clarifying this relationship may inform interventions for these related conditions. Accordingly, data from three annual waves collected from a substantial group of Chinese adolescents (N = 3029, M_age = 12.26 ± 2.36, male: 50.00%) were used to assess within-person and between-person effects in the association between PSU and materialism. Traditional cross-lagged panel models were utilized to analyze the data, which consistently showed reciprocal positive associations between PSU and materialism across all waves. In contrast, the random intercept cross-lagged panel model revealed that PSU and materialism exhibited reciprocal associations over time at the between-person level. However, no significant cross-lagged linkage was observed between PSU and materialism at the within-person level. These findings enhance our understanding of the temporal dynamic relationship between PSU and materialism and underscore the necessity to disaggregate within-person and between-person effects to elucidate the nature of the longitudinal associations between PSU and materialism. The study also has implications for theoretical and practical understanding.

While extensive research has examined adult perceptions of Artificial Intelligence (AI)’s impact on the workforce, studies focusing on adolescents—who are at a critical stage of career development—remain scarce. Drawing upon Social Cognitive Career Theory (SCCT) and the significance of fostering adolescents’ adaptability in the era of AI, we designed a questionnaire-based experiment to examine how adolescents’ prior perceptions, attitude and immediate knowledge of AI influence their career preferences. We conducted a questionnaire-based experiment with 836 adolescents (M_age = 13.98, SD = 1.35 years; 52.30% male) to investigate the influence of three independent variable groups: prior perception of AI (familiarity with AI), prior attitudes towards AI (trust in AI and positive/negative attitude towards AI) and immediate knowledge of AI (experimental manipulation). The results showed that immediate knowledge of AI significantly and negatively predicted adolescents’ preference for both low- and high-replaceable jobs. In contrast, familiarity with AI, trust in AI and positive attitude towards AI significantly and positively predicted a preference for low-replaceable jobs. This study introduces an integrated empirical framework demonstrating that distinct AI-related factors differentially influence adolescent career preferences. Results emphasize the importance of familiarity with AI, trust in AI and positive attitude towards AI among teenagers in order to better adapt the future changes in the labor market. This finding provides an empirical reference for educators and policy makers to better guide teenagers to make career plans.