A pilot study of measuring emotional response and perception of LLM-generated questionnaire and human-generated questionnaires

Zhao Zou, Omar Mubin, Fady Alnajjar, Luqman Ali

Research output: Contribution to journalArticlepeer-review

Abstract

The advent of ChatGPT has sparked a heated debate surrounding natural language processing technology and AI-powered chatbots, leading to extensive research and applications across various disciplines. This pilot study aims to investigate the impact of ChatGPT on users' experiences by administering two distinct questionnaires, one generated by humans and the other by ChatGPT, along with an Emotion Detecting Model. A total of 14 participants (7 female and 7 male) aged between 18 and 35 years were recruited, resulting in the collection of 8672 ChatGPT-associated data points and 8797 human-associated data points. Data analysis was conducted using Analysis of Variance (ANOVA). The results indicate that the utilization of ChatGPT enhances participants' happiness levels and reduces their sadness levels. While no significant gender influences were observed, variations were found about specific emotions. It is important to note that the limited sample size, narrow age range, and potential cultural impacts restrict the generalizability of the findings to a broader population. Future research directions should explore the impact of incorporating additional language models or chatbots on user emotions, particularly among specific age groups such as older individuals and teenagers. As one of the pioneering works evaluating the human perception of ChatGPT text and communication, it is noteworthy that ChatGPT received positive evaluations and demonstrated effectiveness in generating extensive questionnaires.

Original languageEnglish
Article number2781
JournalScientific reports
Volume14
Issue number1
DOIs
Publication statusPublished - Dec 2024

ASJC Scopus subject areas

  • General

Fingerprint

Dive into the research topics of 'A pilot study of measuring emotional response and perception of LLM-generated questionnaire and human-generated questionnaires'. Together they form a unique fingerprint.

Cite this