Title:AI-Powered Intelligent Conference Scheduling System: Optimizing Efficiency and Adaptability


Authors:Ishmeet Kour Bhatia, Mohit Bari, Nidhi Nigam, Chanchal Bansal


Published in: Volume 2 Issue 2 July-December 2025, Page No. 27-32


DOI: https://doi.org/10.63844/IJAITR.v2.i2.2025. 27-32
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Keywords: —Conference scheduling, Artificial intelligence, Machine learning, Natural language processing, Reinforcement learning, GDPR compliance, Resource optimization, Ethical AI


Abstract:Modern conference management involves complex scheduling challenges, including multi-track sessions, speaker availability, and participant preferences. This paper presents ConfAI, an AI-driven scheduling system that integrates machine learning (ML), natural language processing (NLP), and reinforce- ment learning (RL) to optimize conference planning. The system utilizes BERT-based topic modeling for session categorization and Qlearning for efficient resource allocation, significantly reducing scheduling conflicts and improving schedule generation speed. Key features include a constraint satisfaction solver that dynamically adjusts schedules based on real-time changes and a predictive analytics engine for attendance forecasting, enhancing venue space utilization. The system employs federated learning for data privacy and has been successfully implemented in vari- ous conferences, demonstrating improved participant experience and organizational efficiency. Ethical safeguards, including bias mitigation and explainable AI, ensure fairness and transparency in decision-making. ConfAI establishes a scalable framework for AI-driven event management, addressing key challenges in collaborative scheduling.


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