Summary of Prompts Matter: Comparing Ml/gai Approaches For Generating Inductive Qualitative Coding Results, by John Chen et al.
Prompts Matter: Comparing ML/GAI Approaches for Generating Inductive Qualitative Coding Results
by John Chen, Alexandros Lotsos, Lexie Zhao, Grace Wang, Uri Wilensky, Bruce Sherin, Michael Horn
First submitted to arxiv on: 10 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the potential of generative AI (GAI) in qualitative coding processes for education research. The study applies four different approaches to an online community dataset and evaluates their performance. The results show significant discrepancies between traditional ML/GAI methods and highlight the benefits of incorporating human coding processes into GAI prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper demonstrates how AI can be used to generate inductive coding results, which has the potential to streamline the research process. The study finds that introducing human coding processes into GAI prompts leads to better results than traditional ML/GAI approaches. |
