Human-Centered Design in Conversational AI: Optimizing Natural Language Understanding for Diverse Users

Abstract

In conversational artificial intelligence, human-centered design focuses on the creation of interactions that are user-friendly, inclusive, and adaptable to meet the requirements of a wide range of users. natural language understanding (NLU) techniques that are optimized for conversational artificial intelligence, with the goal of ensuring that models are able to effectively interpret and respond to a wide variety of linguistic styles, dialects, and user preferences. We design a natural language understanding (NLU) framework that improves accessibility and engagement for a wider audience by incorporating user feedback, cultural context, and adaptive learning methodologies. The findings of our experiments indicate that our human-centered approach considerably enhances both the level of satisfaction experienced by users and the flow of discussion across all demographics. demonstrates the significance of human-centered design in the process of developing user-friendly artificial intelligence systems for a wide range of populations by fostering inclusivity and improving the naturalness of interactions. This adds to the advancement of conversational artificial intelligence.