Parenting styles and ADHD severity: Leveraging AI to understand their relationship

Abstract

The relationship between parenting styles and the severity of attention deficit hyperactivity disorder (ADHD) in children has garnered considerable attention, yet remains complex and multifaceted. This study aims to elucidate this relationship by leveraging advanced artificial intelligence (AI) techniques to analyse a comprehensive dataset of parenting behaviours and ADHD symptomatology. Utilizing machine learning algorithms, we classified parenting styles into authoritative, authoritarian, permissive, and uninvolved categories based on standardized questionnaires. Simultaneously, ADHD severity was quantified using clinically validated scales. The AI-driven analysis revealed significant correlations between specific parenting styles and ADHD severity, with authoritative parenting showing a negative correlation, suggesting a potential mitigating effect on ADHD symptoms. Conversely, authoritarian and permissive styles were positively correlated with higher ADHD severity, indicating potential exacerbation of symptoms. Uninvolved parenting showed the weakest correlation, yet still highlighted notable impacts. Additionally, AI models identified nuanced patterns and interactions between various parenting practices and ADHD characteristics that traditional statistical methods might overlook. These findings underscore the critical role of adaptive parenting strategies in managing ADHD and highlight the potential of AI to uncover intricate behavioural patterns, offering novel insights for clinicians and researchers. The study advocates for the integration of AI in psychological and behavioural research to enhance the understanding of complex developmental disorders and improve intervention strategies. This approach not only deepens our comprehension of ADHD and its environmental influencers but also sets a precedent for future research utilizing AI to explore psychological phenomena.