This paper reviews the integration of Natural Language Processing (NLP) into social science research, drawing on key concepts from Alan Turing’s Machine Intelligence, Marvin Minsky’s Society of Mind, and Bruno Latour’s Actor-Network Theory. The study identifies key methodologies and applications, emphasizing how NLP can advance social science research through improved task formulation, robust evaluation metrics, and versatile applications across diverse data types. The findings suggest that NLP has significant potential to enhance research processes in the social sciences, particularly in data analysis and agent-based modeling of human behavior. However, challenges remain, including ethical concerns, limited model interpretability, and ongoing debates over NLP’s capacity for genuine reasoning versus mere simulation. To address these issues, the proposed “Reflexive Artificial Intelligence (AI)” framework and agenda call for NLP systems that align with societal values and prioritize ethical considerations in social contexts. This study aims to provide an in-depth exploration of the intersection between AI and social sciences, examining how these technologies can enhance research on societal issues and inform future AI applications for social good.