Artificial Intelligence in the automatic coding of interviews on Landscape Quality Objectives. Comparison and case study

• URL: http://arxiv.org/abs/2312.05597v1
• Date: Sat, 9 Dec 2023 15:37:19 GMT
• Title: Artificial Intelligence in the automatic coding of interviews on Landscape Quality Objectives. Comparison and case study
• Authors: Mario Burgui-Burgui
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we conducted a comparative analysis of the automated coding provided by three Artificial Intelligence functionalities (At-las.ti, ChatGPT and Google Bard) in relation to the manual coding of 12 research interviews focused on Landscape Quality Objectives for a small island in the north of Cuba (Cayo Santa Mar\'ia). For this purpose, the following comparison criteria were established: Accuracy, Comprehensiveness, Thematic Coherence, Redundancy, Clarity, Detail and Regularity. The analysis showed the usefulness of AI for the intended purpose, albeit with numerous flaws and shortcomings. In summary, today the automatic coding of AIs can be considered useful as a guide towards a subsequent in-depth and meticulous analysis of the information by the researcher. However, as this is such a recently developed field, rapid evolution is expected to bring the necessary improvements to these tools.

Related papers

• The AI Co-Ethnographer: How Far Can Automation Take Qualitative Research? [51.40252017262535]
  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.
  arXiv  Detail & Related papers  (2025-04-21T21:31:28Z)
• General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
  benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems. We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure. Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
  arXiv  Detail & Related papers  (2025-03-09T01:13:56Z)
• Long-Span Question-Answering: Automatic Question Generation and QA-System Ranking via Side-by-Side Evaluation [65.16137964758612]
  We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Our objective is to test the capabilities of LLMs to analyze, understand, and reason over problems that require a detailed comprehension of long spans of text.
  arXiv  Detail & Related papers  (2024-05-31T20:15:10Z)
• Adaptation of XAI to Auto-tuning for Numerical Libraries [0.0]
  Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations.
  arXiv  Detail & Related papers  (2024-05-12T09:00:56Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• 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)
• Artificial Intelligence for Literature Reviews: Opportunities and Challenges [0.0]
  This manuscript presents a comprehensive review of the use of Artificial Intelligence in Systematic Literature Reviews. A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features.
  arXiv  Detail & Related papers  (2024-02-13T16:05:51Z)
• Artificial intelligence to automate the systematic review of scientific literature [0.0]
  We present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies.
  arXiv  Detail & Related papers  (2024-01-13T19:12:49Z)
• Around the GLOBE: Numerical Aggregation Question-Answering on Heterogeneous Genealogical Knowledge Graphs with Deep Neural Networks [0.934612743192798]
  We present a new end-to-end methodology for numerical aggregation QA for genealogical trees. The proposed architecture, GLOBE, outperforms the state-of-the-art models and pipelines by achieving 87% accuracy for this task. This study may have practical implications for genealogical information centers and museums.
  arXiv  Detail & Related papers  (2023-07-30T12:09:00Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• AANG: Automating Auxiliary Learning [110.36191309793135]
  We present an approach for automatically generating a suite of auxiliary objectives. We achieve this by deconstructing existing objectives within a novel unified taxonomy, identifying connections between them, and generating new ones based on the uncovered structure. This leads us to a principled and efficient algorithm for searching the space of generated objectives to find those most useful to a specified end-task.
  arXiv  Detail & Related papers  (2022-05-27T16:32:28Z)
• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs [62.71505254770827]
  We propose a conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts. Our model obtains impressive performance gains over all baselines on both tasks, using only a fraction of data for training.
  arXiv  Detail & Related papers  (2020-05-28T08:26:06Z)
• SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information [83.03179580646324]
  This paper proposes a novel deep neural network architecture, namely SideInfNet. It integrates features learnt from images with side information extracted from user annotations. To evaluate our method, we applied the proposed network to three semantic segmentation tasks and conducted extensive experiments on benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-07T06:10:54Z)

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.