Use Generative Al and Natural Language Processing to Improve User Interaction Design

Abstract

Modern artificial intelligence technologies continue to mature, and the applications of generative AI and natural language processing (NLP) have become increasingly widespread across various intelligent systems. This study systematically examines the fundamental principles, technical characteristics, and internal connections between generative AI models and NLP mechanisms, forming the theoretical foundation for constructing a next-generation intelligent interaction system. On this basis, the paper designs and develops an integrated system architecture featuring semantic parsing, instant content generation, cross-modal adaptation, continuous self-adjustment, and closed-loop feedback. Through the combined application of intent recognition, generative reasoning, and multimodal information fusion strategies, the system enhances its capability to understand user needs, improves interaction fluency, and significantly strengthens the naturalness and stability of human-machine communication. The experimental component of this work focuses on three representative application scenarios: intelligent customer service agents, voice-based interactive terminals, and text generation and writing assistants. Leveraging extensive comparative experimental data, the study evaluates the performance of the proposed system in terms of response accuracy, generation quality, latency, user comfort, and interaction smoothness. The results demonstrate that the incorporation of generative AI and self-evolving NLP techniques can effectively enhance response speed, improve user experience, and elevate the overall effectiveness of interactive processes. Overall, the findings indicate that human-machine interaction systems built on generative AI and integrated NLP possess high degrees of intelligence, adaptability, and perceptive capability. These systems provide promising prospects for practical deployment and broader promotion across multiple industry sectors, offering valuable guidance for the development of future intelligent interactive applications.