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Artificial intelligence (AI)-assisted coding : grounded theory analysis of leader development at the United States Air Force Academy / Cortny Stark, Connor Scroggins.
- Format:
- Book
- Author/Creator:
- Stark, Cortny, author.
- Scroggins, Connor, active 2024, author.
- Series:
- SAGE research method cases.
- SAGE research method cases
- Language:
- English
- Subjects (All):
- United States Air Force Academy.
- Grounded theory--United States.
- Grounded theory.
- Artificial intelligence.
- Physical Description:
- 1 online resource.
- Place of Publication:
- London : SAGE Publications Ltd, 2024.
- Summary:
- Grounded theory is a recursive approach characterized by multiple in-depth reviews of text-based data, with researchers identifying and labeling emerging themes and related processes. This coding process requires constant comparison, holding in mind both the nuances associated with frequently occurring codes and their relationships as well as novel themes unique to particular participants or contexts. Artificial intelligence (AI)-based tools provide a promising, timesaving approach to the coding process. This methods case study describes the use of ATLAS.ti's AI-based coding as part of the data-analysis process for the Cadet Leader Development Study (n = 16). The case study focuses on the use of the grounded theory approach, with AI-based support to analyze qualitative data. In alignment with this method, the researchers engaged in multiple reviews of text-based data, identifying and labeling codes (also known as categories) and subcodes. Researchers collaborated with the qualitative data-analysis platform ATLAS.ti's OpenAI, with AI serving as a fourth coder. This case study provides one example of the use of AI-assisted coding as part of a grounded theory study. Methodologic implications discussed include constant comparison process when using AI-assisted coding, limitations of AI-based coding, advantages of AI support, and ethical considerations.
- Notes:
- Description based on XML content.
- ISBN:
- 1-5296-8078-6
- 9781529680782
- OCLC:
- 1418720703
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