AI-Assisted Qualitative Data Interpretation Using LLMs for Key Point Detection

Abstract

Qualitative data interpretation plays a pivotal role in understanding human perceptions, behaviors, and contextual nuances across domains such as social sciences, healthcare, education, and marketing. However, the manual coding and interpretation of qualitative data remain labor-intensive, subjective, and time-consuming. This research introduces an AI-assisted approach utilizing Large Language Models (LLMs) to automate key point detection in qualitative data. The study explores how transformer-based architectures, specifically GPT-style LLMs, can identify, cluster, and summarize key insights from unstructured textual data with high interpretive accuracy. Through empirical evaluation on multiple open-ended survey datasets and interview transcripts, the system demonstrates its capability to emulate human-level thematic analysis while maintaining scalability and consistency. The proposed framework combines semantic embeddings, context-sensitive reasoning, and probabilistic filtering to enhance interpretability and trustworthiness. Results reveal that the LLM-based interpretation mechanism achieves a 91.4% alignment rate with human-coded themes, suggesting its potential for revolutionizing qualitative data analysis.