<![CDATA[The increasing use of conversational artificial intelligence in casual and academic communication raises significant questions about how such systems create interpersonal meaning through language. Limited research has been conducted in terms of linguistic mechanisms through which interpersonal relationships are established across different registers. Addressing this gap, the current study explores how ChatGPT creates interpersonal meaning in informal and formal academic interactions through the lens of the systemic functional linguistics (SFL) framework. This qualitative SFL-based analysis research examines 10 ChatGPT-generated responses to prompts that were purposely designed, comprising five casual and five academic interactions that were broken down into 86 clauses and analyzed through the lens of the interpersonal metafunction, focusing on mood, modality, and appraisal, facilitated by register theory and the three dimensions of human-machine communication (HMC). The findings reveal a clear variation in the use of ChatGPT's interpersonal strategies depending on the register. Casual interactions exhibit a higher number of appraisal resources, especially affect and engagement, which reflect a relational and user-oriented stance. In contrast, academic interactions are described by the predominance of declarative mood and medium to high. This study provides insight into how AI imitates human behavior, such as interactional roles through language selection. The findings highlight the importance of register sensitivity in AI-human communication and have implications for linguistics, language education, and chatbot design. It is suggested that further studies should consider investigating larger datasets, more registers, and comparative analyses of different AI models.]]>
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