Abstract: This paper presents an innovative ensemble method for developing a student policy chatbot that leverages advanced natural language processing techniques, specifically an extractive question-answering approach combined with a large language model (LLM). The study aims to enhance the chatbot’s ability to accurately understand and respond to student inquiries regarding university policies and procedures. Utilizing a dataset derived from the University Handbook and Code of Decorum for Genderbased Sexual Harassment, the chatbot architecture integrates the RoBERTa model for extractive answering and GPT-2 for natural language generation. Evaluation metrics, including automatic assessments and human evaluations, reveal the chatbot’s strengths in relevance and coverage, while also identifying areas for improvement in precision and coherence. The findings underscore the potential of AI-driven chatbots in higher education to enhance student engagement and support, while recommendations for future enhancements are necessary to ensure the chatbot’s effectiveness in addressing the diverse needs of students.