Dr. Raskirat Kaur

Artificial Intelligence (AI) is transforming mental health service delivery within public health systems, offering innovative tools for assessment, intervention, and policy-level decision-making. Children with special needs—including those with developmental, learning, and neurodiverse conditions—often face challenges in accessing timely mental health support, leading to delayed identification of emotional and behavioral difficulties and widening disparities in care. AI-driven mental health support provides opportunities to bridge these gaps by enabling early detection, personalized interventions, and continuous monitoring of psychological well-being. Aligned with the World Health Organization (WHO) framework, which emphasizes mental health promotion, prevention, early intervention, and community-based care, AI applications such as predictive analytics, digital screening tools, and adaptive therapeutic platforms can enhance the accuracy and efficiency of mental health assessments. These technologies also allow mental health professionals and educators to monitor progress, adjust interventions in real time, and provide scalable support in school and community settings. Similarly, the National Education Policy (NEP) 2020 highlights inclusive education, early identification of learning difficulties, and the integration of technology to provide personalized learning experiences. AI can support these goals by facilitating individualized educational and psychological plans, improving access to assistive technologies, and enhancing engagement for learners with special needs. While the potential benefits of AI are significant, ethical, legal, and policy challenges must be addressed. Issues such as data privacy, algorithmic bias, informed consent, equitable access, and over-reliance on technology require careful consideration. Human oversight, interdisciplinary collaboration, and evidence-based regulation are critical to ensuring that AI tools complement, rather than replace, traditional mental health services. This research work aims to explore AI-driven mental health support from a public health perspective, emphasizing the integration of technological innovation with ethical practice, inclusive education, and policy frameworks. By examining the intersection of AI, mental health, and special education, the discussion seeks to advance strategies for responsible, equitable, and effective mental health support for children with special needs, ensuring their holistic development and psychological well-being at a population level.

Seasonal Affective Disorder (SAD) is a recurrent subtype of depression associated with seasonal shifts, particularly reduced daylight during autumn and winter. Although gender differences in depression are widely documented, comparatively less attention has been given to how changing climatic patterns affect these gender disparities. Climate change—manifesting through rising temperature variability, altered seasonal transitions, and irregular sunlight exposure—may intensify or reshape SAD symptoms. This paper examines gender-specific vulnerability, symptom expression, and psychosocial pathways linking climate-related seasonal changes to depressive outcomes. Drawing from biopsychosocial and climate-psychology frameworks, this analysis integrates empirical findings and conceptual arguments to show how women and men experience seasonal depression differently, while also acknowledging emerging evidence on nonbinary individuals who remain understudied. The findings suggest that women exhibit higher SAD prevalence and symptom severity, while men show distinct behavioural responses. Climate change appears to magnify these disparities through circadian disruption, stress exposure, and socioeconomic pressures. Implications include the need for gender-responsive mental-health interventions, climate-informed screening tools, and policy measures addressing environmental determinants of mood disorders. The purpose of this paper is to explore how gender differences in seasonal depression intersect with climate-related environmental changes. Through a review of empirical research and conceptual models along with an experimental study on 200 adults, this paper examines biological, psychological, and social pathways that contribute to gender-differentiated vulnerability to SAD within a changing climate.