The objective of positive education is not only to improve students’ well-being but also their academic performance. As an important concept in positive education, growth mindset refers to core assumptions about the malleability of a person’s intellectual abilities. The present study investigates the relation of growth mindsets to psychological well-being and school engagement. The study also explores the mediating function of resilience in this relation. We recruited a total of 1260 (658 males and 602 females) Chinese students from five diversified primary and middle schools. Results from the structural equation model (SEM) show that the development of high levels of growth mindsets in students predicts higher psychological well-being and school engagement through the enhancement of resilience. The current study contributes to our understanding towards the potential mechanisms of how positive education (e.g. altering the mindset of students) can impact psychological well-being and school engagement.

Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on “well-being”, then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34,p = 0.007 andt(59) = 4.32,p < 0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.

This review explores the frontiers of large language models (LLMs) in psychological applications. Psychology has undergone several theoretical changes, and the current use of artificial intelligence (AI) and machine learning, particularly LLMs, promises to open up new research directions. We aim to provide a detailed exploration of how LLMs are transforming psychological research. We discuss the impact of LLMs across various branches of psychology—including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology—highlighting their ability to model patterns, cognition, and behavior similar to those observed in humans. Furthermore, we explore the ability of such models to generate coherent, contextually relevant text, offering innovative tools for literature reviews, hypothesis generation, experimental designs, experimental subjects, and data analysis in psychology. We emphasize the importance of addressing technical and ethical challenges, including data privacy, the ethics of using LLMs in psychological research, and the need for a deeper understanding of these models’ limitations. Researchers should use LLMs responsibly in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, this review provides a comprehensive overview of the current state of LLMs in psychology, exploring the potential benefits and challenges. We hope it can serve as a call to action for researchers to responsibly leverage LLMs’ advantages while addressing the associated risks.

By investigating the use of first-person pronouns in nine languages using the Google Ngram Database, we examined the degree to which different cultural values skewed toward individualism or collectivism over a span of 59 years. We found that in eight of nine languages (British English being the exception), first-person singular pronouns (vs. first-person plural pronouns) have become increasingly prevalent, which in turn points to a rising sense of individualism. British English showed a U-shaped curve trend in the use of first-person singular pronouns (vs. first-person plural pronouns). Although they initially decreased, British English’s first-person singular pronouns (vs. first-person plural pronouns) use was higher than most other languages throughout the whole period. Chinese displayed a fluctuating pattern wherein the use of first-person singular pronouns (vs. first-person plural pronouns) increased in recent periods. The dynamics of cultural change and culture diversity were discussed.

Sociocultural research has established independence and interdependence as two fundamental ways of thinking about oneself and the social world. Recent neuroscience studies further demonstrate that these orientations modulate brain activity in various self- and socially related tasks. In the current study, we explored whether the traits of independence and interdependence are reflected in anatomical variations in brain structure. We carried out structural brain imaging on a large sample of healthy participants (n = 265) who also completed self-report questionnaires of cultural orientations. Voxel-based morphometry analysis demonstrated that a relative focus of independence (vs. interdependence) was associated with increased gray-matter volume in a number of self-related regions, including ventromedial prefrontal cortex, right dorsolateral prefrontal cortex, and right rostrolateral prefrontal cortex. These results provide novel insights into the biological basis of sociocultural orientations.