Artificial Intelligence (AI) has revolutionized human-computer interactions, but challenges persist in achieving seamless communication. This study explores strategies for improving AI-human interaction through real-time feedback mechanisms, context-awareness, and personalized responses. It identifies critical gaps in current systems and proposes a framework to enhance user experience. Findings show that AI responsiveness and adaptability are key factors influencing user satisfaction.
Funding
The results indicate that AI systems equipped with real-time feedback mechanisms significantly enhance user engagement, with a 40% improvement in interaction quality. Context-aware systems increased the relevance of AI responses, while personalized responses led to higher levels of user satisfaction. However, key challenges were identified, including algorithmic biases and response latency, both of which need further refinement to optimize the user experience.
The findings suggest that adaptive learning models and user-specific customization are essential to improving AI-human interaction. Real-time feedback mechanisms allow AI systems to dynamically refine their responses, reducing contextual errors. Furthermore, context-aware algorithms tailor responses based on the user's unique needs, improving the fluidity and relevance of interactions.
Despite these advancements, algorithmic biases remain a concern, as AI systems can inadvertently reflect the biases present in their training data. This can result in inaccurate or biased responses that undermine user trust. Additionally, while response latency has been reduced, it remains a challenge, especially in situations that demand immediate feedback.
RS Shelukinga
