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Transparent Reporting of Generative AI Use in Academic Writing: A Cardwriter Tool
核心概念
A tool to streamline and automate the generation of PaperCards - a standardized documentation for authors to transparently report their use of generative AI in the academic writing process.
摘要
The content discusses the growing use of generative AI and large language models (LLMs) in the academic writing process, despite the lack of a unified framework for reporting such machine assistance. To address this, the authors propose "Cardwriter" - an intuitive interface that generates a short "PaperCard" report for authors to declare their use of generative AI in their writing process.
The system consists of three main components: a user interface that allows authors to select the type of machine assistance used and the specific models, a processor that generates the body of the PaperCard based on the user input, and a display that presents the final PaperCard in a format ready for inclusion in the manuscript.
The intended use of the system is for authors who either did not use any generative AI assistance or those who did, across academic and non-academic writing domains. The authors discuss the limitations of the user-oriented declaration approach, as well as the positive societal implications of providing a practical framework for transparent reporting of machine assistance in writing.
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arxiv.org
Three Disclaimers for Safe Disclosure: A Cardwriter for Reporting the Use of Generative AI in Writing Process
統計資料
With recent advancements in generative AI, researchers are increasingly using these tools in the academic writing process.
There is a lack of unified framework for reporting the use of machine assistance in academic writing.
The authors propose Cardwriter to streamline the generation of PaperCards - a standardized documentation for transparent reporting of generative AI use.
引述
"There exists a gap between technological advances and the maturity of the level of public acceptance. As a result, we often see public backlash when people find out that machine assistance was used in creating a piece of work, or when the use was not explicitly declared."
"Simply not using any machine assistance in the writing process is not practical. Especially in modern days where English has become a de facto language in academic writing, using assistance of not only generative AI but also tools like Grammarly, Quillbot, Google Translate or DeepL are inevitable for improving the quality of writing."
深入探究
How can the system be extended to cover a wider range of machine assistance beyond just generative AI, such as for creating figures, tables, and supplementary materials?
To extend the system to cover a wider range of machine assistance, including tasks like creating figures, tables, and supplementary materials, the following steps can be taken:
System Expansion: The system architecture can be modified to incorporate modules specifically designed for handling different types of machine assistance. For instance, a module for generating figures could be added, which allows users to input parameters or data and receive generated visual representations.
User Interface Enhancements: The user interface can be updated to include options for selecting the type of machine assistance required, such as figure generation, table creation, or supplementary material development. This would provide users with a more comprehensive tool for reporting various forms of machine assistance.
Processor Adaptation: The processor component of the system would need to be adjusted to accommodate the processing of different types of machine-generated content. Templates and dictionaries can be expanded to support the generation of reports specific to figures, tables, and supplementary materials.
Data Dictionary Expansion: The system's data dictionary can be expanded to include information on various machine assistance tools beyond generative AI, such as figure generation software, table creation algorithms, and supplementary material generators. This would enable the system to provide accurate and detailed reports for a wider range of machine assistance.
Community Contribution: Encouraging community contributions to update the system with new machine assistance tools and functionalities can help in keeping the system relevant and up-to-date. Users can suggest new tools to be included in the system, ensuring its adaptability to evolving technologies.
What are the potential risks and unintended consequences of mandating transparent reporting of machine assistance in academic writing, and how can they be mitigated?
Mandating transparent reporting of machine assistance in academic writing can have several risks and unintended consequences, including:
Misinterpretation: There is a risk of readers misinterpreting the use of machine assistance as a lack of originality or creativity on the part of the author. This could lead to unwarranted criticism or skepticism towards the work.
Stigmatization: Authors who rely on machine assistance may face stigmatization or bias from peers or reviewers who perceive such reliance negatively. This could impact their reputation and career prospects.
Ethical Concerns: There may be ethical concerns regarding the ownership of machine-generated content and the potential for plagiarism or intellectual property disputes. Clear guidelines and regulations are needed to address these issues.
Burden on Authors: Mandating transparent reporting could place an additional burden on authors, especially those who are not familiar with the technical aspects of machine assistance. This could deter some authors from using such tools altogether.
To mitigate these risks and unintended consequences, the following measures can be taken:
Education and Awareness: Providing education and awareness programs to authors, reviewers, and readers about the benefits and limitations of machine assistance can help in fostering a better understanding and acceptance of its use.
Standardized Guidelines: Developing standardized guidelines for reporting machine assistance in academic writing can ensure consistency and clarity in disclosures. These guidelines should be clear, concise, and easily accessible to all stakeholders.
Peer Review Oversight: Implementing peer review processes that include experts in machine learning and AI can help in evaluating the use of machine assistance objectively and providing constructive feedback to authors.
Ethics Committees: Establishing ethics committees or review boards to address ethical concerns related to machine assistance in academic writing can ensure that all issues are properly considered and resolved.
How might the use of generative AI in academic writing impact the development of critical thinking and original ideas among students and early-career researchers?
The use of generative AI in academic writing can have both positive and negative impacts on the development of critical thinking and original ideas among students and early-career researchers:
Positive Impact:
Efficiency: Generative AI tools can help students and researchers generate ideas and content quickly, allowing them to focus more on analysis and interpretation.
Creativity: AI-generated suggestions can inspire new perspectives and creative approaches to problem-solving, fostering innovation and originality.
Collaboration: Collaborating with AI systems can enhance teamwork skills and encourage interdisciplinary research, leading to novel ideas and solutions.
Negative Impact:
Overreliance: Excessive reliance on generative AI may hinder the development of critical thinking skills, as users may become dependent on the tool for idea generation.
Bias: AI models may perpetuate biases present in the training data, potentially limiting the diversity of ideas and perspectives explored by students and researchers.
Plagiarism: Inadvertent inclusion of AI-generated content without proper attribution could lead to unintentional plagiarism, undermining the development of original ideas.
To mitigate these potential negative impacts and enhance the positive effects of generative AI on critical thinking and originality, it is essential to:
Promote Education: Educate users on the proper use of AI tools, emphasizing the importance of critical thinking and independent idea generation.
Encourage Experimentation: Encourage students and researchers to experiment with AI tools as aids rather than replacements for their own thinking processes.
Provide Guidance: Offer guidelines on ethical AI use, plagiarism prevention, and proper attribution to ensure the integrity of academic work.
Foster Mentorship: Facilitate mentorship programs where experienced researchers can guide students and early-career researchers in balancing AI assistance with critical thinking and originality.
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