The collective use and perceptions of generative AI tools in digital humanities research: Survey-based results

• URL: http://arxiv.org/abs/2404.12458v2
• Date: Mon, 07 Oct 2024 18:07:54 GMT
• Title: The collective use and perceptions of generative AI tools in digital humanities research: Survey-based results
• Authors: Meredith Dedema, Rongqian Ma,
• Abstract summary: Generative artificial intelligence technologies have revolutionized the research landscape, with significant implications for Digital Humanities. This article investigates how DH scholars adopt and critically evaluate generative AI technologies such as ChatGPT in research.
• Score: 0.6906005491572401
• License:
• Abstract: Generative artificial intelligence technologies have revolutionized the research landscape, with significant implications for Digital Humanities, a field inherently intertwined with technological progress. This article investigates how DH scholars adopt and critically evaluate generative AI technologies such as ChatGPT in research. Drawing on 76 responses collected from an international survey study, we explored DH scholars' rationale for adopting or not adopting generative AI tools in research, identified the specific practices of using generative AI tools to support various DH research tasks, and analyzed scholars' collective perceptions regarding the benefits, risks, and challenges of using generative AI tools in DH research. The survey results reveal two key findings: first, DH research communities hold divisive opinions about the value of generative AI in DH scholarship; second, scholars have developed new practices and perceptions for using generative AI tools, which differ from those associated with traditional AI-based tools. Our survey represents one of the first survey-based analyses on this topic. It has the potential to serve as a building block for future empirical inquiries into the impact of generative AI on DH scholarship.

Related papers

• Measuring Human Contribution in AI-Assisted Content Generation [68.03658922067487]
  This study raises the research question of measuring human contribution in AI-assisted content generation. By calculating mutual information between human input and AI-assisted output relative to self-information of AI-assisted output, we quantify the proportional information contribution of humans in content generation.
  arXiv  Detail & Related papers  (2024-08-27T05:56:04Z)
• Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies [0.0]
  This study examines the impact of Generative Artificial Intelligence (GenAI) on academic research, focusing on its application to qualitative and quantitative data analysis. GenAI tools evolve rapidly, they offer new possibilities for enhancing research productivity and democratising complex analytical processes. Their integration into academic practice raises significant questions regarding research integrity and security, authorship, and the changing nature of scholarly work.
  arXiv  Detail & Related papers  (2024-08-13T13:10:03Z)
• Higher education assessment practice in the era of generative AI tools [0.37282630026096586]
  This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.
  arXiv  Detail & Related papers  (2024-04-01T10:43:50Z)
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape [5.852005817069381]
  The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
  arXiv  Detail & Related papers  (2023-12-18T01:11:39Z)
• ChatGPT and Beyond: The Generative AI Revolution in Education [0.21756081703275998]
  This survey examines academic literature published between November, 2022 and July, 2023. It aims to illuminate the evolving role of generative AI models, particularly ChatGPT, in education. The findings of this review will empower educators, researchers, and policymakers to make informed decisions about the integration of AI technologies into learning environments.
  arXiv  Detail & Related papers  (2023-11-26T05:34:22Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
  In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
  arXiv  Detail & Related papers  (2023-02-16T08:59:42Z)
• 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)
• Characterising Research Areas in the field of AI [68.8204255655161]
  We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics. The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
  arXiv  Detail & Related papers  (2022-05-26T16:30:30Z)
• Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies [22.214709837295906]
  There is growing interest in the research community to augment human decision making with AI assistance. The emerging field of human-AI decision making must embrace empirical approaches to form a foundational understanding of how humans interact and work with AI.
  arXiv  Detail & Related papers  (2021-12-21T19:00:02Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)

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.