The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research?
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Abstract (arXiv)
Qualitative research often involves labor-intensive processes that are difficult to scale while preserving analytical depth. This paper introduces The AI Co-Ethnographer (AICoE), a novel end-to-end pipeline developed for qualitative research and designed to move beyond the limitations of simply automating code assignments, offering a more integrated approach. AICoE organizes the entire process, encompassing open coding, code consolidation, code application, and even pattern discovery, leading to a comprehensive analysis of qualitative data.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 36 references that are in the map. First posted on arXiv 21 Apr 2025.
Builds on
9 of 36 references.
References · 9
- 2024The Llama 3 Herd of Modelscited by 14,715
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
- 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
- 2023Benchmarking Large Language Models for News Summarizationcited by 445
- 2020Super-Human Performance in Online Low-latency Recognition of Conversational Speechcited by 20
- 2024From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptionscited by 16
- 2024Zero-Shot Strategies for Length-Controllable Summarizationcited by 10
- 2024MSA-ASR: Efficient Multilingual Speaker Attribution with frozen ASR Modelscited by 4
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Citing papers per year: 2026: 1.
1 paper.