As the prominence of Large Language Models (LLMs) grows in various sectors, their potential in the educational realm warrants exploration. This study investigates the feasibility of employing GPT-3.5, an LLM developed by OpenAI, as a virtual teaching assistant (TA) in computer science courses. The primary objective is to enhance the accessibility of computer science education while maintaining academic integrity by refraining from providing direct solutions to assignment questions. To achieve this goal, a virtual TA was developed using LangChain, a sophisticated and data-aware framework for applications powered by language models, known for its ability to interact with diverse data sources and environments. We selected GPT-3.5 for its exceptional contextual understanding and capability to generate nuanced responses, ensuring its suitability for the task. The study focused on the Foundations of Programming undergraduate course (CS1), where the virtual TA was designed to provide coding feedback, address student inquiries, and clarify intricate concepts. Our findings revealed that the virtual TA outperformed human TAs in providing more detailed responses to sample student questions. The system successfully abstained from disclosing current semester assignment solutions. However, in cases where specific assignment requirements were queried, human TAs proved more reliable. Our research demonstrates that virtual TAs hold great promise for enriching computer science education, but their optimal use necessitates human supervision. By leveraging the capabilities of LLMs within the LangChain framework, we can pave the way for a more accessible and effective educational experience in the digital era.