This study presents a novel Artificial Intelligence (AI)-based approach to automate conference session scheduling that integrates Natural Language Processing (NLP) techniques. By analyzing contextual information from conference activities, this framework uses a Constraint Satisfaction Problem (CSP) model to optimize the scheduling. Using data from the MSR conference, and by employing GPT-3.5 and SciBERT, this framework generates a weighted contextual similarity and applies hard constraints, to ensure scheduling feasibility, and soft constraints, to improve thematic coherence. Evaluations demonstrate the ability to surpass manual scheduling.