This paper provides a comprehensive review of the use of ChatGPT in software requirements engineering (SRE). The authors analyze the role of large language models (LLMs), such as ChatGPT-3.5, in SRE, a critical area of software engineering undergoing rapid advancements due to artificial intelligence (AI). By examining several studies, the authors systematically evaluate the integration of ChatGPT into SRE, focusing on its benefits, challenges, and ethical considerations. The evaluation is based on a comparative analysis that highlights ChatGPT's efficiency in eliciting requirements, accuracy in capturing user needs, potential to improve communication among stakeholders, and impact on the responsibilities of requirements engineers. The selected studies were analyzed for their insights into the effectiveness of ChatGPT, the importance of human feedback, prompt engineering techniques, technological limitations, and future research directions in using LLMs in SRE. This comprehensive analysis aims to provide a differentiated perspective on how ChatGPT can reshape SRE practices and provides strategic recommendations for leveraging ChatGPT to effectively improve the SRE process. The paper discusses the potential benefits of using ChatGPT in SRE, including improving brainstorming and idea exploration, continuous learning, and minimizing human error. It also addresses the challenges and ethical considerations associated with the use of ChatGPT in SRE, such as the potential for inaccuracies, ambiguities, or biased outputs, and the need for careful examination of data privacy, model bias, and transparency. The authors emphasize the importance of prompt engineering in guiding LLMs to produce outputs that align with their intended objectives. They also highlight the critical role of human feedback in ensuring the quality of requirements generated by ChatGPT. The paper concludes with future research directions, emphasizing the need for further exploration of the potential of LLMs in SRE.This paper provides a comprehensive review of the use of ChatGPT in software requirements engineering (SRE). The authors analyze the role of large language models (LLMs), such as ChatGPT-3.5, in SRE, a critical area of software engineering undergoing rapid advancements due to artificial intelligence (AI). By examining several studies, the authors systematically evaluate the integration of ChatGPT into SRE, focusing on its benefits, challenges, and ethical considerations. The evaluation is based on a comparative analysis that highlights ChatGPT's efficiency in eliciting requirements, accuracy in capturing user needs, potential to improve communication among stakeholders, and impact on the responsibilities of requirements engineers. The selected studies were analyzed for their insights into the effectiveness of ChatGPT, the importance of human feedback, prompt engineering techniques, technological limitations, and future research directions in using LLMs in SRE. This comprehensive analysis aims to provide a differentiated perspective on how ChatGPT can reshape SRE practices and provides strategic recommendations for leveraging ChatGPT to effectively improve the SRE process. The paper discusses the potential benefits of using ChatGPT in SRE, including improving brainstorming and idea exploration, continuous learning, and minimizing human error. It also addresses the challenges and ethical considerations associated with the use of ChatGPT in SRE, such as the potential for inaccuracies, ambiguities, or biased outputs, and the need for careful examination of data privacy, model bias, and transparency. The authors emphasize the importance of prompt engineering in guiding LLMs to produce outputs that align with their intended objectives. They also highlight the critical role of human feedback in ensuring the quality of requirements generated by ChatGPT. The paper concludes with future research directions, emphasizing the need for further exploration of the potential of LLMs in SRE.