Impact of Human-AI Interaction on User Trust and Reliance in AI-Assisted Qualitative Coding

TL;DR

This study investigates how different human-AI interaction strategies affect user trust and reliance in AI-assisted qualitative coding, highlighting the importance of tailored system design and addressing reliance discrepancies.

Contribution

It introduces a comprehensive analysis of interaction strategies in AIQCs, emphasizing subtask-specific design and trust-reliance dynamics in qualitative coding.

Findings

Qualitative open coding involves subtasks with varying complexity.

Discrepancies exist between perceived trust and actual reliance.

Design considerations can mitigate over- and under-reliance issues.

Abstract

While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop a trustworthy AI-assisted qualitative coding system (AIQCs) that supports coding tasks effectively. We bridge this gap by exploring the impact of varying coding strategies on user trust and reliance on AI. We conducted a mixed-methods split-plot 3x3 study, involving 30 participants, and a follow-up study with 6 participants, exploring varying text selection and code length in the use of our AIQCs system for qualitative analysis. Our results indicate that qualitative open coding should be conceptualized as a series of distinct subtasks, each with differing levels of complexity, and therefore, should be given tailored design considerations. We further observed a discrepancy between perceived and behavioral measures, and emphasized the potential challenges of under- and over-reliance on AIQCs systems. Additional design implications were also proposed for consideration.