ChatGPT has the potential to enhance academic research efficiency and outcomes. Using the GPT model for initial coding in qualitative thematic analysis, Aleksei Turobov, Diane Coyle and Verity Harding outline the advantages and limitations of using the GPT model and suggest strategies for mitigating risks.
The utilisation of AI-driven tools, notably ChatGPT (Generative Pre-trained Transformer), within academic research is increasingly debated from several perspectives including ease of implementation, and potential enhancements in research efficiency, as against ethical concerns and risks such as biases and unexplained AI operations.
This paper explores the use of the GPT model for initial coding in qualitative thematic analysis using a sample of United Nations (UN) policy documents. The primary aim of this study is to contribute to the methodological discussion regarding the integration of AI tools, offering a practical guide to validation for using GPT as a collaborative research assistant. The paper outlines the advantages and limitations of this methodology and suggests strategies to mitigate risks.
Emphasising the importance of transparency and reliability in employing GPT within research methodologies, this paper argues for a balanced use of AI in supported thematic analysis, highlighting its potential to elevate research efficacy and outcomes.
