{"id":264107,"date":"2024-03-28T13:51:23","date_gmt":"2024-03-28T17:51:23","guid":{"rendered":"https:\/\/rosenfeldmedia.com\/sessions\/qualitative-synthesis-with-chatgpt\/"},"modified":"2024-09-09T10:54:19","modified_gmt":"2024-09-09T14:54:19","slug":"qualitative-synthesis-with-chatgpt","status":"publish","type":"sessions","link":"https:\/\/rosenfeldmedia.com\/sessions\/qualitative-synthesis-with-chatgpt\/","title":{"rendered":"[Case Study] Qualitative synthesis with ChatGPT: Better or worse than human intelligence?"},"content":{"rendered":"

Following the emergence of Generative AI as a potential revolution in the UX field, a great deal of AI-driven tools arose to enhance the efficiency of UX research, including data analysis. Qualitative data analysis is a process that conventionally relies on human intelligence to discern patterns, establish connections, and derive actionable insights and frameworks. Many studies have involved comparing the quality of qualitative analyses generated by humans with those produced by AI language models like ChatGPT (Hamilton et al., 2023).<\/p>\n

Despite the undeniable appeal of automation and speed, there is ongoing debate about AI\u2019s ability to replace human intelligence in qualitative analysis, which may be unlikely at this moment. Then the question is: To what extent can AI contribute to qualitative data analysis?<\/p>\n

In this case study, I delved into the thematic analysis and post-analysis stage, i.e. synthesizing insights into a framework. Framework, in this context, refers to a conceptual structure that illustrates the components of a human experience and how the components interconnect and operate within the structure. It is a concise model that encapsulates the entirety of research insights.<\/p>\n

The topic of my case study is “trust relationships between job seekers and hirers in the marketplace,, aligning with the business focus of my company. From my secondary research, I found that, ChatGPT needed multiple rounds of training using diverse prompts to conduct precise and comprehensive thematic analysis. ChatGPT can execute fine-quality thematic analysis under the help of right prompts, yet it falls short in replacing human intelligence for synthesizing insights and crafting frameworks for engaging narratives.<\/p>\n

Its limitation lies in lacking the depth of contextual understanding within a company, such as understanding what\u2019s missing from the company\u2019s mainstream discourse to create a human-centered story based on data analysis. To craft a framework that conveys good storytelling and organizational impact, it requires the researcher’s introspection into knowledge gaps in the specific organizational context. Thus, the best practice is to combine human interpretation and AI production. In my talk, I will demonstrate several principles to guide this practice.<\/p>\n

Takeaways<\/strong><\/p>\n

We\u2019ll cover principles of how to employ ChatGPT in qualitative analysis, specifically focusing on its application in synthesizing and crafting frameworks that convey compelling and insightful narratives:<\/p>\n