Toward a New Level of Human-Chatbot Communication: Goal Management and Mutual Verbal Adaptation

As artificial intelligence becomes increasingly integrated into everyday communication, understanding the dynamics of human-chatbot interaction has become a matter of both theoretical importance and practical urgency. This study explores the goals, communicative tactics, and adaptive strategies employed by users and AI chatbots in dialogue, using grounded theory methodology. Based on a corpus of 316 dialogues with ChatGPT, we conducted multi-level coding - substantive, selective, and theoretical - to identify recurring patterns in the organization of digital communication. The analysis revealed a wide range of user goals, including informational, task-oriented, generative, emotional, and exploratory intentions. Chatbots, in turn, pursued structurally narrower but functionally adaptive goals aimed at supporting dialogue coherence and user engagement. Both sides employed diverse communicative tactics, including primary, combined, and compensatory strategies. While users initiated goal setting and frequently adjusted their tactics, chatbots demonstrated reactive behavior through clarification, tone adaptation, and metacommunicative responses. A key result is the identification of six basic communicative scenarios in user-chatbot interaction: informational-analytical, practical, creative, emotional-reflective, entertaining-playful, and exploratory-provocative. Each scenario reflects a stable alignment of goals and tactics between the participants, revealing the functional architecture of digital dialogue. The study demonstrates that interaction with generative chatbots is not random, but unfolds within structured communicative configurations. These findings contribute to the theoretical understanding of digital interaction and provide a typological framework for analyzing, designing, and optimizing AI-based communication systems across various domains.