AI-Driven Mobile Usage Tracking and Management System for Youth
Patel Kartik Rameshbhai, Khyati Zalawadia, Mukesh Patidar · 2025
The concerns about the effects of excessive use on mental health, productivity and well-being emerged in response to the growing dependence of young people on mobile devices. Intelligent options are the furniture surveillance and management systems that use the artificial intelligence (AI). These systems use automatic learning and behavioral analysis to obtain the time when people transmit on the screens, how frequency uses general applications and habits. To promote responsible use of smartphones, these systems provide real information, personalized suggestions and adaptive interventions. The use notifications, controls and analysis of parents who may predict, if a person is too used are important aspects. Although these solutions with the best digital consciousness and self -regulation, there are still important obstacles to overcome, including concerns for the confidentiality, data security and users' conformity. In this paper optimize intervention tactics, future developments can focus on the inclusion of emotional intelligence, better adaptive learning and the most perfect human interactions. The monitoring systems promoted by AI can help reduce the harmful effects of the use of cell phones in teenagers and to promote adequate online behavior when solving these problems. In this paper, the presented work was demonstrated by Python 3.12 software, and the results of three models with the best precision rates were obtained by the random forest (RF) (85.37%), the decisionmaking tree (DT) (85.13%), and logistic regression (LR) (84.35%).