Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that can affect individuals across lifespan and is characterized by inattention, hyperactivity, and impulsivity. Traditional ADHD diagnostic methods commonly rely on subjective assessments, leading to potential inaccuracies. This study proposes a novel mobile game that utilizes a combined continuous screening paradigm (CCSP), integrating classical psychological measures such as the Continuous Performance, Simon, and Go/No-Go tasks to provide a 4-minute ADHD screening tool for children. The serious game screening design focuses on the two dimensions of sustained attention, and hyperactivity-impulsivity in ADHD and was verified using a test comparing children with ADHD and neurotypical children. The screening model achieved a sensitivity of 0.82. This study provides a methodological framework for leveraging serious games on mobile platforms to facilitate early ADHD screening.

Promoting positive mental health and well-being, especially in adolescents, is a critical yet underexplored area in natural language processing (NLP). Most existing NLP research focuses on clinical therapy or psychological counseling for the general population, which does not adequately address the preventative and growth-oriented needs of adolescents. In this paper, we introduce DeepWell-Adol, a domain-specific Chinese dialogue corpus grounded in positive psychology and coaching, designed to foster adolescents’ positive mental health and well-being. To balance the trade-offs between data quality, quantity, and scenario diversity, the corpus comprises two main components: human expert-written seed data (ensuring professional quality) and its mirrored expansion (automatically generated using a two-stage scenario-based augmentation framework). This approach enables large-scale data creation while maintaining domain relevance and reliability. Comprehensive evaluations demonstrate that the corpus meets general standards for psychological dialogue and emotional support, while also showing superior performance across multiple models in promoting positive psychological processes, character strengths, interpersonal relationships, and healthy behaviors. Moreover, the framework proposed for building and evaluating DeepWell-Adol offers a flexible and scalable method for developing domain-specific datasets. It significantly enhances automation and reduces development costs without compromising professional standards—an essential consideration in sensitive areas like adolescent and elderly mental health. We make our dataset publicly available.

Psychological resilience refers to an individual's ability to adapt to adversity and stress. Education on psychological resilience during childhood can contribute to future mental health and well-being, such as reducing anxiety and depression [1] [2]. However, traditional psychosocial resilience training faces challenges with accessibility, heavily constrained by cost and spatiotemporal limitations. Recently, emerging large language models (LLMs) have demonstrated exceptional capabilities in conversational tasks, indicating new prospects for cultivating children's psychological resilience. In our work, 1) we conducted qualitative interviews with 10 Chinese children (aged 8-12) and their parents to understand their needs and current conditions; 2) based on the interview results and theories of psychological resilience, we summarized three pathways for developing children's psychological resilience using conversational agents (CAs) and identified six key challenges for designing child-centered CAs; 3) we designed and developed a web prototype using optimized LLMs (see Figure 1), which integrates personal and social support factors, to measure and foster children's psychological resilience through conversations; and 4) we invited 48 child volunteers in user testing and designed three sets of experiments to evaluate the effectiveness of system interventions, the effectiveness of measurements, and overall acceptability. Results indicate that the intervention tasks actively promoted psychological resilience in adolescents. Intelligent measurement scores were effectively consistent with traditional scales in objective scoring, while subjective evaluations, such as appeal and fun, significantly exceeded traditional scale scores.

Adolescents face much psychological stress in the current social environment, and effective emotional regulation is crucial to their mental health. This article introduces a paradigm of product-oriented psychological dialogue, that is, to study psychological problems first, determine user needs and the most effective way of action, and then develop tools based on this paradigm. We use the above paradigm to build an artificial intelligence-based adolescent emotion adjust the con-versational bot. Specifically, to explore adolescents' emotional regulation needs, this study collected the required data (n=317, 5,543 questionnaires) through the intensive tracking method. It revealed the mechanism of user needs and emotion regulation. Emotion regulation strategy weighting mechanism, and using the collected raw data and existing emotion support dialogue datasets (ESConv), a Chinese adolescent emotion regulation dia-logue dataset was constructed. After that, this paper fine-tunes the existing dialogue model (GPT-2 chitchat). Through these improvements, the dialogue model has dramatically improved its performance and can also provide more personalized and effective emotional regulation support according to the actual needs of adolescents. In summary, this study provides new ideas and methods for mental health support, and promotes the research and development of emotional regulation support for adolescents.