Evaluating Conversational AI Models for Emotional Intelligence: Techniques for Enhanced User Engagement

Abstract

Enhancing these models with emotional intelligence (EI) has become crucial for raising user pleasure and engagement as conversational AI is incorporated more and more into customer service, healthcare, and education. The ability of conversational AI models with emotional intelligence techniques to identify, decipher, and react to users' emotional cues is the main focus. To evaluate their effect on user engagement, we investigate a number of strategies, such as tone detection, sentiment analysis, and adaptive response generation. We investigate the effectiveness of these EI-enhanced models in establishing constructive connections, building user trust, and preserving organic, sympathetic conversations through empirical testing. The results suggest potential for applications that prioritize user experience, since models incorporating emotional intelligence techniques significantly outperform classical conversational AI in terms of user pleasure. the creation of emotionally intelligent AI, which provides information on practical methods for conversational agents seeking to engage in more meaningful and interesting interactions.