ChatGPTest: opportunities and cautionary tales of utilizing AI for questionnaire pretesting
This addresses a gap in research methodology for survey designers, but it is incremental as it builds on existing AI applications without major breakthroughs.
The paper tackles the problem of survey questionnaire pretesting by exploring the use of GPT models as a tool to provide feedback in early design stages, suggesting it can reduce iterations before human pretesting.
The rapid advancements in generative artificial intelligence have opened up new avenues for enhancing various aspects of research, including the design and evaluation of survey questionnaires. However, the recent pioneering applications have not considered questionnaire pretesting. This article explores the use of GPT models as a useful tool for pretesting survey questionnaires, particularly in the early stages of survey design. Illustrated with two applications, the article suggests incorporating GPT feedback as an additional stage before human pretesting, potentially reducing successive iterations. The article also emphasizes the indispensable role of researchers' judgment in interpreting and implementing AI-generated feedback.