ChatGPTest: Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting
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| Title: | ChatGPTest: Opportunities and Cautionary Tales of Utilizing AI for Questionnaire Pretesting |
|---|---|
| Language: | English |
| Authors: | Francisco Olivos (ORCID |
| Source: | Field Methods. 2025 37(4):277-290. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Artificial Intelligence, Questionnaires, Test Construction, Pretesting, Feedback (Response), Researchers, Role |
| DOI: | 10.1177/1525822X241280574 |
| ISSN: | 1525-822X 1552-3969 |
| Abstract: | The rapid advancements in generative artificial intelligence have opened 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1488081 |
| Database: | ERIC |
| Abstract: | The rapid advancements in generative artificial intelligence have opened 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. |
|---|---|
| ISSN: | 1525-822X 1552-3969 |
| DOI: | 10.1177/1525822X241280574 |
