COMMUNICATION STRATEGIES AND EFFECTS OF AI-DRIVEN CHATBOT
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Abstract
This project work examines the communication strategies employed by AI-driven chatbots and their effects on user interactions. In an era where conversational AI systems such as ChatGPT, Meta AI, Google Assistant, Gemini, and Replika are increasingly integrated into everyday communication, understanding how these systems shape human-machine interactions has become crucial. The purpose of this study was to analyze the communicative behaviors of chatbots through the lens of Grice's Cooperative Principle and the Social Presence Theory, with the aim of identifying the strategies they use, assessing their effectiveness, and evaluating their influence on user trust, engagement, and satisfaction. The motivation for this research arises from the growing role of chatbots not only as information providers but also as relational companions, raising questions about both their benefits and their risks in human communication. Chatbot-user conversations were collected and critically analyzed to evaluate how responses adhered to Grice's maxims of Quantity, Quality, Relation, and Manner, while also examining how chatbots projected social presence through empathy, personalization, and human-like responses. The study also identified communication strategies such as personalization, empathy framing, positive reframing, immediacy of response, and explanatory clarity. The findings
revealed that chatbots generally adhere to the Cooperative Principle by providing relevant, clear, and concise responses, though occasional lapses occur in the form of repetition and overgeneralized statements. Through Social Presence Theory, the study found that chatbots successfully simulate human-like warmth and empathy, creating a sense of companionship and
trust for users. The research recommends that chatbot developers integrate more adaptive communication strategies that balance factual accuracy with empathy and personalization. By doing so, AI-driven systems can strengthen both the cognitive and emotional dimensions of user interaction, leading to improved satisfaction, trust, and long-term adoption.
revealed that chatbots generally adhere to the Cooperative Principle by providing relevant, clear, and concise responses, though occasional lapses occur in the form of repetition and overgeneralized statements. Through Social Presence Theory, the study found that chatbots successfully simulate human-like warmth and empathy, creating a sense of companionship and
trust for users. The research recommends that chatbot developers integrate more adaptive communication strategies that balance factual accuracy with empathy and personalization. By doing so, AI-driven systems can strengthen both the cognitive and emotional dimensions of user interaction, leading to improved satisfaction, trust, and long-term adoption.
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