<![CDATA[Requirements analysis is a critical phase in the development of information systems, as it significantly influences the overall success of a system. However, traditional approaches to requirements analysis are often performed manually and are prone to errors, inconsistencies, and inefficiencies. The advancement of Artificial Intelligence (AI) provides new opportunities to improve the effectiveness and automation of this process. This study aims to analyze the integration of AI in requirements analysis using a Systematic Literature Review (SLR) approach. The review follows the PRISMA 2020 guidelines and examines relevant studies published between 2020 and 2025. A total of 14 selected articles were analyzed to identify commonly used AI techniques, evaluate their effectiveness, and explore existing challenges. The results indicate that various AI techniques, including Machine Learning, Deep Learning, Transformer-based models, and Large Language Models (LLMs), have been widely applied in requirements analysis tasks such as classification, ambiguity detection, information extraction, and prioritization. These techniques demonstrate improvements in accuracy, time efficiency, and consistency compared to conventional methods. Despite these advantages, several challenges remain, including data imbalance, limited model generalization, lack of explainability, and limited validation in real-world industrial environments. Therefore, further research is needed to enhance the reliability and applicability of AI-based approaches in practical settings.]]>
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