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Generative AI: Is Authentic Qualitative Research Data Collection Possible?

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Title: Generative AI: Is Authentic Qualitative Research Data Collection Possible?
Language: English
Authors: Cheryl Burleigh (ORCID 0000-0003-2393-5477), Andrea M. Wilson (ORCID 0000-0002-1471-654X)
Source: Journal of Educational Technology Systems. 2024 53(2):89-115.
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: 27
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Data Collection, Doctoral Dissertations, Research Methodology, Natural Language Processing, Integrity, Educational Policy, Higher Education
DOI: 10.1177/00472395241270278
ISSN: 0047-2395
1541-3810
Abstract: With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular attention to the use of GAI in collecting new data for a doctoral dissertation. This study employed qualitative methodology examining how GAI, specifically ChatGPT, responded to interview questions from a previously published article by the researchers to determine how closely chatbots mimic responses from the actual study participants. The researchers found that data integrity in qualitative research may be at risk if higher education institutions do not set clear policies and specific parameters for how doctoral research data is obtained and validated in light of GAI.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1443789
Database: ERIC
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  Data: With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular attention to the use of GAI in collecting new data for a doctoral dissertation. This study employed qualitative methodology examining how GAI, specifically ChatGPT, responded to interview questions from a previously published article by the researchers to determine how closely chatbots mimic responses from the actual study participants. The researchers found that data integrity in qualitative research may be at risk if higher education institutions do not set clear policies and specific parameters for how doctoral research data is obtained and validated in light of GAI.
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