Human-Independent AI-Based Productivity Enforcer

S. Rishi Vardhan¹, K. Chaithanya², G. Kishore³, M. Vandana⁴
Supervisor: Dr.M.V. Krishna Mohan, Assistant Professor, Dept. of CSE, VIET
¹Department of CSE (AIML), Visakha Institute of Engineering and Technology, Andhra Pradesh, India
2Department of CSE (AIML), Visakha Institute of Engineering and Technology, Andhra Pradesh, India
3Department of CSE (AIML), Visakha Institute of Engineering and Technology, Andhra Pradesh, India
4Department of CSE (AIML), Visakha Institute of Engineering and Technology, Andhra Pradesh, India

Abstract - This paper presents a Human-Independent AI Based Productivity Enforcer designed to improve user productivity productivity by automatically restricting access to distracting applications and activating focus mode when necessary. Unlike traditional productivity tools that rely on userdiscipline, the through intelligent monitoring and automated  control mechanisms. The system analyses user application usage patterns in real time and identifies non-productive behavior using machine learning techniques such as K-Means clustering. Based on this analysis, applications are classified intoproductive and non-productive categories. A threshold-based  decision mechanism is applied to enforce proposed systemensures consistent enforcement through automation. The system  is implemented using an Android-based interface integrated with backend services and locally executed AI models to ensure privacy and efficiency. Experimental results demonstrate a significant reduction in non-productive screen time and improved user focus. The proposed approach provides a scalable and adaptive solution for enhancing productivity amongstudents and professionals.
Key Words: Artificial Intelligence, Productivity Enforcement,App Usage Monitoring, K-Means Clustering, Focus Mode,Behavior Analysis