Using ChatGPT-4 for the Identification of Common UX Factors within a Pool of Measurement Items from Established UX Questionnaires

TL;DR

This paper explores using ChatGPT-4 to analyze and cluster measurement items from 40 UX questionnaires, aiming to identify common UX factors and improve understanding of UX research structures.

Contribution

The study demonstrates that ChatGPT-4 can effectively classify and filter UX measurement items, aiding in the identification of common UX factors across diverse questionnaires.

Findings

ChatGPT-4 successfully clusters similar UX items into meaningful topics.

It can filter items related to specific UX concepts from large item pools.

The approach enhances understanding of UX measurement structures.

Abstract

Measuring User Experience (UX) with standardized questionnaires is a widely used method. A questionnaire is based on different scales that represent UX factors and items. However, the questionnaires have no common ground concerning naming different factors and the items used to measure them. This study aims to identify general UX factors based on the formulation of the measurement items. Items from a set of 40 established UX questionnaires were analyzed by Generative AI (GenAI) to identify semantically similar items and to cluster similar topics. We used the LLM ChatGPT-4 for this analysis. Results show that ChatGPT-4 can classify items into meaningful topics and thus help to create a deeper understanding of the structure of the UX research field. In addition, we show that ChatGPT-4 can filter items related to a predefined UX concept out of a pool of UX items.