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

## TL;DR

This paper introduces SADMA, an AI-based method for analyzing conversational misalignments to reveal interpersonal dynamics in dialogue.

## Contribution

SADMA combines information theory and dialogism with AI to systematically detect conversational deflections and misalignments.

## Key findings

- SADMA identifies deflection points where expected conversational trajectories break down.
- The method reveals patterns of relational conflict and miscommunication in dialogues.
- SADMA offers objective insights into subjective meaning-making in human interactions.

## Abstract

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.