Psychology

BackgroundArtificial intelligence (AI) is increasingly integrated into health care, including psychiatry and psychology. In educational contexts, AI offers new possibilities for enhancing clinical reasoning, personalizing content delivery, and supporting professional development. Despite this emerging interest, a comprehensive understanding of how AI is currently used in mental health education, and the challenges associated with its adoption, remains limited.ObjectiveThis scoping review aimed to identify and characterize current applications of AI in the teaching and learning of psychiatry and psychology. It also sought to document reported facilitators of and barriers to the integration of AI within educational contexts.MethodsA systematic search was conducted across 6 electronic databases (MEDLINE, PubMed, Embase, PsycINFO, EBM Reviews, and Google Scholar) from inception to October 2024. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Studies were included if they focused on psychiatry or psychology, described the use of an AI tool, and discussed at least 1 facilitator of or barrier to its use in education. Data were extracted on study characteristics, population, AI application, educational outcomes, facilitators, and barriers. Study quality was appraised using several design-appropriate tools.ResultsFrom 6219 records, 10 (0.2%) studies met the inclusion criteria. Eight categories of AI applications were identified: clinical decision support, educational content creation, therapeutic tools and mental health monitoring, administrative and research assistance, natural language processing (NLP), program/policy development, students' study aid, and professional development. Key facilitators included the availability of AI tools, positive learner attitudes, digital infrastructure, and time-saving features. Barriers included limited AI training, ethical concerns, lack of digital literacy, algorithmic opacity, and insufficient curricular integration. The overall methodological quality of included studies was moderate to high.ConclusionsAI is being used across a range of educational functions in psychiatry and psychology, from clinical training to assessment and administrative support. Although the potential for enhancing learning outcomes is clear, its successful integration requires addressing ethical, technical, and pedagogical barriers. Future efforts should focus on AI literacy, faculty development, and institutional policies to guide responsible and effective use. This review underscores the importance of interdisciplinary collaboration to ensure the safe, equitable, and meaningful adoption of AI in mental health education.

UnlabelledRecent applications of artificial intelligence (AI) and machine learning in medicine, psychology, and social sciences have led to common terminological confusions. In this paper, we review emerging evidence from systematic reviews documenting widespread misuse of key terms, particularly "prediction" being applied to studies merely demonstrating association or retrospective analysis. We clarify when "prediction" should be used and recommend using "prospective prediction" for future prediction; explain validation procedures essential for model generalizability; discuss overfitting and generalization in machine learning and traditional regression methods; clarify relationships between features, independent variables, predictors, risk factors, and causal factors; and clarify the hierarchical relationship between AI, machine learning, deep learning, large language models, and generative AI. We provide evidence-based recommendations for terminology use that can facilitate clearer communication among researchers from different disciplines and between the research community and the public, ultimately advancing the rigorous application of AI in medicine, psychology, and social sciences.

Scientific research is often characterized by schools of thought. We investigate whether these divisions are associated with differences in researchers' cognitive traits such as tolerance for ambiguity. These differences may guide researchers to prefer different problems, tackle identical problems in different ways, and even reach different conclusions when studying the same problems in the same way. We surveyed 7,973 researchers in psychological sciences and investigated links between what they research, their stances on open questions in the field, and their cognitive traits and dispositions. Our results show that researchers' stances on scientific questions are associated with what they research and with their cognitive traits. Further, these associations are detectable in their publication histories. These findings support the idea that divisions in scientific fields reflect differences in the researchers themselves, hinting that some divisions may be more difficult to bridge than suggested by a traditional view of data-driven scientific consensus.

This article synthesizes cutting-edge research from our special issue examining power across groups, communities and nations to advance a fundamental reconceptualization that reveals power's inherent plurality, dynamism and cultural embeddedness. Drawing on evidence from a diverse set of countries and innovative methodologies, we demonstrate that traditional definitions of power as asymmetric resource control fail to capture its real-world complexity. The collected research reveals three critical insights. First, power operates through multiple, intersecting mechanisms-from ideological frameworks like hegemonic masculinity to seemingly benevolent helping behaviours that maintain hierarchies. Second, power's meaning and operation vary dramatically across cultural contexts, with the power-status relationship ranging from nearly synonymous in some societies to entirely disconnected in others. Third, marginalized groups develop sophisticated alternative conceptualizations of power as collective resistance, cultural preservation and mutual aid that enable transformation despite structural disadvantages. These findings necessitate theoretical frameworks that accommodate power's plurality while identifying general principles, examining dynamic processes rather than static attributes and integrating multiple levels of analysis. We argue for moving beyond imposed definitions to understand how diverse groups conceptualize their own agency, examining not just domination but resistance and developing interventions that promote more equitable power distributions.