Misunderstanding and User dissatisfaction in AI-Driven Chatbot Interactions with Consumers.

In today's world, artificial intelligence (AI) chatbots are a part of customer service in almost every industry. Unfortunately, user dissatisfaction and miscommunication issues are commonly reported even with their use. This research explores the primary drivers of user dissatisfaction in the interaction with AI chatbots, specifically looking into query misinterpretation, lack of emotional recognition, absence of personalized feedback, as well as privacy and data security issues. With a qualitative approach, the study conducted semi-structured interviews with twelve participants from the e-commerce, banking, education, and customer service sectors. Theme-based analysis led to the identification of four major themes: poor interpretation of user instructions, inappropriate or insufficient accuracy responsiveness, absence of empathy, and excessive concern for privacy. The participants pointed out that while chatbots are sufficient in performing mundane tasks, they do not perform very well when it comes to complex and emotional tasks. The study advocates for better chatbot design using sophisticated natural language processing (NLP), emotional AI, fortified security measures, and smoother handoff to a human agent. Enhancing these features is crucial toward achieving greater user satisfaction and effectiveness of AI systems in customer interactions. The results highlight the gaps in empathetic, contextual understanding and privacy-respectful security that chatbots incorporate in reference to the changing needs of users.