The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research?

• URL: http://arxiv.org/abs/2505.00012v1
• Date: Mon, 21 Apr 2025 21:31:28 GMT
• Title: The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research?
• Authors: Fabian Retkowski, Andreas Sudmann, Alexander Waibel,
• Abstract summary: The AI Co-Ethnographer (AICoE) is a novel end-to-end pipeline developed for qualitative research.<n>AICoE organizes the entire process, encompassing open coding, code consolidation, code application, and even pattern discovery.
• Score: 51.40252017262535
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration [3.1861081539404137]
  This paper explores the application of structured prompt patterns to minimize the number of interactions required for satisfactory AI-assisted code generation.<n>We analyzed seven distinct prompt patterns to evaluate their effectiveness in reducing back-and-forth communication between developers and AI.
  arXiv  Detail & Related papers  (2025-06-02T12:43:08Z)
• A Computational Method for Measuring "Open Codes" in Qualitative Analysis [47.358809793796624]
  Open coding is an inductive qualitative process that identifies and interprets "open codes" from datasets. We present a computational method to measure and identify potential biases from "open codes" systematically.
  arXiv  Detail & Related papers  (2024-11-19T00:44:56Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing.<n>Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering.<n>This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Automating the Information Extraction from Semi-Structured Interview Transcripts [0.0]
  This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. We present a user-friendly software prototype that enables researchers to efficiently process and visualize the thematic structure of interview data.
  arXiv  Detail & Related papers  (2024-03-07T13:53:03Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis [6.592797748561459]
  Large Language Models (LLMs) have enabled collaborative human-bot interactions in Software Engineering (SE) We introduce a new dimension of scalability and accuracy in qualitative research, potentially transforming data interpretation methodologies in SE.
  arXiv  Detail & Related papers  (2024-02-02T13:10:46Z)
• Artificial Intelligence in the automatic coding of interviews on Landscape Quality Objectives. Comparison and case study [0.0]
  Three Artificial Intelligence functionalities (Atlas.ti, ChatGPT and Google Bard) were compared. The analysis showed the usefulness of AI for the intended purpose, albeit with numerous flaws and shortcomings.
  arXiv  Detail & Related papers  (2023-12-09T15:37:19Z)
• Towards Automated Process Planning and Mining [77.34726150561087]
  We present a research project in which researchers from the AI and BPM field work jointly together. We discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models.
  arXiv  Detail & Related papers  (2022-08-18T16:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.