Recommendation system for anti-procrastination tracking based on ChatGPT and personal data

The purpose of the research is to analyze the potential of integrating the ChatGPT language model into a personalized recommendation system aimed at reducing procrastination. The focus is on the concept of a digital assistant capable of adaptively responding to users’ behavioral patterns, maintaining attention, and fostering self-discipline in the context of information overload and constant digital distractions. Methods . The system is based on the ChatGPT language model integrated with user activity trackers. It is designed to collect and analyze data such as task schedules, productivity levels, mood, task-switching frequency, and physiological parameters (e.g., heart rate, stress level, sleep quality) where wearable devices are available. The system architecture includes a contextual layer for data aggregation and a dialogue generation module for personalized recommendations. Recommendations are stratified into operational (immediate actions), tactical (daily planning), and motivational (focus and resilience support) types. The system also provides mechanisms for adapting recommendations based on behavioral context, temporal patterns, and user state. Results . The paper describes the functional components of the proposed architecture, key user interaction scenarios, and examples of dialogue interventions. Interface solutions for visualizing progress in productivity and self-regulation are discussed. The feasibility of using a language model as an empathetic digital coach that can respond to users’ dynamic states and provide supportive guidance for overcoming procrastination is substantiated. Conclusion . The proposed concept demonstrates the potential of ChatGPT as a tool for digital well-being. Personalized interaction based on the analysis of user behavior and states may serve as a foundation for the development of effective digital self-regulation systems. Future research directions include the implementation of predictive logic, integration of biometric tracking, and expansion of the behavioral model.