This thesis investigates how users perceive intelligence in AI chatbots and identifies the factors that influence this perception. By examining how user input styles and interaction design impact perceived intelligence and response accuracy, This research bridges a gap in the literature through a user centric design approach, the study explores the dynamics of user interactions with chatbots. Key findings reveal that user driven control, context aware interactions, and real time feedback are pivotal in shaping user perceptions of chatbot intelligence. The research highlights the importance of balancing user guidance with user control, ensuring chatbots appear intelligent without increasing cognitive load. Additionally, this thesis introduces a feedback mechanism that improves input quality, improving user satisfaction and interaction clarity. These insights provide a practical framework for designing more effective and intelligent conversational AIsystems, contributing to the ongoing development of chatbot interactions.