Fostering Positive Childhood Behaviour and Psychological Growth through Proactive Art Education

Disruptive behaviour and conduct difficulties threaten children's development, resulting in academic impairment, special education use, high school dropout, substance misuse, and antisociality. When disruptive behaviours begin in primary school, these issues escalate, making early intervention crucial. New advancements are making instructional practices more accessible and appealing, while current ones require structural alterations to make them more acceptable to educators. Due to enhanced school procedures and technologies, this study examines proactive screening for behaviorally at-risk students. CNN-DBAM, a convolutional neural network-based disruptive behaviour analysis architecture, can analyze features and extract behavioural information from children's paintings. The convolution layer's nonlinear activation function and other efficient methods improve this model's early identification and support. According to CNN-DBAM, proactive art education boosts good behaviour and psychological growth. In the interest of early detection and intervention, this paper examines how art-based screening enhances school psychologist training. According to the author, proactive art instruction in schools may improve early screening and psychological assistance.