The concept of artificial intelligence (AI) was first proposed at an international conference in 1956. In recent years, AI has undergone rapid development and has become deeply integrated into various aspects of daily life. This progress has been driven by advances in computer science and the accumulation of large-scale data, leading to its widespread adoption as a practical and versatile technology in society. In particular, the proliferation of conversational AI and automation technologies has significantly influenced human decision-making and behavioral patterns, to the extent that AI now functions as an integral part of everyday life. In this special issue, we present three contributions from experts who are conducting research and implementing AI at the forefront of stress science, clinical practice, and occupational health. These articles provide valuable and insightful perspectives on the application of AI in this rapidly evolving field. To further promote the effective use of AI in society, it is essential to establish a collaborative relationship between technology and humans. While concerns about the replacement of humans by AI continue to be discussed, this special issue emphasizes the importance of exploring how AI can be utilized to augment human capabilities.

This review explores the application of artificial intelligence (AI) for stress detection. Chronic stress poses substantial health risks and remains a persistent societal issue. While effective prevention requires accurate and timely assessment, current evaluation methods often rely on subjective indicators, which can be biased under high cognitive load and are difficult to deploy for real-time feedback. The integration of AI technology with physiological and physical signals holds promise for addressing the challenges associated with subjective indicators. Specifically, we discuss how deep learning has advanced the field by shifting from manual feature engineering to representation learning, enabling the extraction of complex patterns directly from raw data. Despite these advancements, major challenges remain, including performance degradation in real-world settings, the “black box” nature of models, and privacy concerns. In response, we examine emerging solutions such as multimodal sensing, explainable AI (XAI), and Federated Learning, alongside the potential role of large language models (LLMs). We conclude by discussing the future transition from experimental validation to practical application, highlighting just-in-time adaptive interventions (JITAIs) as a key framework.

Various approaches to workers’ mental health have been implemented, including stress check systems, one-on-one meetings, and support by occupational physicians and employee assistance programs (EAPs). However, challenges remain, such as reliance on once-yearly assessments, person-dependent practices, and barriers related to cost and accessibility. In recent years, advances in digital transformation (DX), accelerated by the COVID-19 pandemic, have led to the rapid expansion of artificial intelligence (AI) applications in the field of occupational health. This article provides an overview of the current status and research trends in the use of AI for workers’ mental health, including online consultations, generative AI, and machine-learning-based predictive models. In particular, drawing on emerging evidence regarding the empathic capabilities of generative AI, the practical value of AI-assisted consultation is examined. Furthermore, using Welwork, an AI platform developed by the authors, as an example, this article introduces a decision-support framework that integrates individual support with organizational improvement.

Cognitive behavioral therapy (CBT) has demonstrated efficacy for a wide range of mental disorders; however, access to treatment remains limited due to a shortage of trained professionals and institutional constraints. This article traces the development of digital CBT (dCBT) against this backdrop and examines recent advances in CBT support utilizing generative artificial intelligence (AI) and large language models (LLMs). Three primary applications are discussed: chatbots, personalization, and recommendation systems, with representative research findings presented for each. AI-powered CBT shows promise as an early intervention tool within stepped care models. Nevertheless, the therapeutic alliance—characterized by the capacity to share uncertainty—remains a uniquely human function that AI cannot replicate. Moving forward, it will be essential to distinguish between outcome indicators common to both human-delivered and digital CBT, such as symptom reduction and adherence, and those specific to human therapists, such as therapeutic alliance. This perspective may inform future research and clinical implementation of AI-assisted mental health interventions.

Caring for maternal psychological stress plays a crucial role in preventing child maltreatment. Social support is widely recognized as a protective factor that contributes to the alleviation of psychological stress. Depending on the source, social support can be classified into formal and informal types. Previous studies have reported that while informal support effectively reduces psychological stress among mothers raising children, formal support does not show the same effect. However, the underlying reasons for this difference remain unclear.

To address this issue, the present study conducted a cross-sectional online survey of 1889 mothers raising preschool-aged children and analyzed the data. The results suggested that formal support may be less effective in alleviating psychological stress due to its higher usage costs and limited capacity to respond to urgent needs. On the other hand, formal support also plays a role in providing opportunities for social interaction, which may help to enhance informal support resources. These findings indicate that although formal support may not directly alleviate psychological stress, it may contribute indirectly by strengthening informal support. Further empirical investigation is warranted to clarify this possibility.

The purpose of this study is to evaluate the development quality and content validity of patient-reported outcome measures (PROMs) of help-seeking skills for children and adolescents. A systematic review was performed in accordance with the COSMIN methodologies. Of the 1362 studies, seven measurement studies met inclusion criteria. Most of the extracted PROMs were characterized by asking about general tendencies without specifying the nature or partner of the help-seeking. Results suggested that none of the PROMs could be recommended for use. Inadequate development quality depended on the unclear measurement concept, the lack of confirmation of correspondence between its measurement concept and items in designing the PROM, and the absence of appropriate cognitive interviews. Poor content validity was evident across all PROMs against a background of non-party involvement.

The purpose of this study is to develop the brief inventory for role stressor (BIRS) that is going to measure role conflict, role ambiguity, and role overload. In addition, we will examine the reliability and validity of the BIRS. The subjects were 920 full-time employees in sales or clerical work, 419 (45.5%) male and 501 (54.5%) female. Exploratory factor analysis resulted in 8-items and two-factors: role unclearly and role overload. The Cronbach’s alpha of the total scale and two components were .88. .84, and .81, respectively, which indicated a favorable concurrent validity. The scale developed in this study is useful for measuring the stress of pharmacists working full-time as white-collar employees.

This study aims to investigate an integrative model that explores how two types of self-focused attention—rumination on negative aspects of the self and reflection involving deeper exploration of the self—affect depression among adolescents, mediated by two dimensions of self-esteem. Specifically, the study examines how contingent self-worth, which depends on external standards, and a sense of authenticity, which reflects a feeling of being true to oneself, mediate these relationships. A total of 183 Japanese university students participated in the survey. Self-focused attention was assessed using measures of rumination and reflection; self-esteem was evaluated through scales of authenticity and contingency of self-worth; and depression was measured with the Zung Self-Rating Depression Scale. Structural equation modeling demonstrated a direct positive association between rumination and depressive symptoms, as well as an indirect negative association between reflection and depressive symptoms, with authenticity functioning as a mediating variable. These findings suggest that higher levels of reflection characterized by authenticity and lower levels of rumination may tend to be associated with reduced depressive symptoms in adolescents.