An AI-Supported Methodology for Analyzing Deflections and Misalignments in Human Interactions

This paper introduces Speech Act Deflection and Misalignment Analysis (SADMA), an AI-assisted methodology for identifying conversational misalignments that reveal underlying interpersonal dynamics. Grounded in a "meaning-as-a-response" framework-combining Conant's information theory and Bakhtin's dialogism-SADMA analyzes utterance-response pairs to detect deflection points where expected conversational trajectories break down. By leveraging a Large Language Model to identify speech acts and goals, SADMA offers objective insights into subjective meaning-making. Applied to Noël Coward's Private Lives, the method highlights patterns of relational conflict and miscommunication. SADMA provides a systematic tool for analyzing conversational breakdowns in psychology, social science, and literary studies.