Further Explorations on the Use of Large Language Models for Thematic Analysis. Open-Ended Prompts, Better Terminologies and Thematic Maps

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Reading and SummarizationDomain Specialization in LLMs

There is a nascent area, where scholars are approaching thematic analysis (TA) using LLMs, following the six phases developed by BRAUN and CLARKE (2006). TA is a qualitative method of analysis where the researcher labels (codes) portions of data with relevant meaning and then organises these codes/labels into patterns (the themes). BRAUN and CLARKE stipulated that TA encompasses the following phases: 1. familiarisation with the data; 2. initial coding; 3. identification of themes; 4. revision of themes; 5. renaming and summarising of themes; and 6. write-up of the results.

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What reasoning limitations and biases do language models systematically exhibit? When should retrieval be triggered to augment language model reasoning? What determines when personalization helps versus harms model behavior? Can AI systems adequately substitute for or supplement human peer review? How do LLMs generate false citations that fool readers and evaluators? What is the actual bottleneck preventing effective AI research automation? How can we better validate AI research and prevent misconceptions?