Techniques for supercharging academic writing with generative AI
- URL: http://arxiv.org/abs/2310.17143v3
- Date: Mon, 12 Aug 2024 20:15:49 GMT
- Title: Techniques for supercharging academic writing with generative AI
- Authors: Zhicheng Lin,
- Abstract summary: This Perspective maps out principles and methods for using generative artificial intelligence (AI) to elevate the quality and efficiency of academic writing.
We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.
Related papers
- Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
Article explores the convergence of connectionist and symbolic artificial intelligence (AI)
Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic.
Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
arXiv Detail & Related papers (2024-07-11T14:00:53Z) - The Impact of AI on Academic Research and Publishing [0.6144680854063939]
Generative artificial intelligence (AI) technologies like ChatGPT have significantly impacted academic writing and publishing.
This paper examines ethical considerations surrounding the integration of AI into academia, focusing on the potential for this technology to be used for scholarly misconduct.
The findings highlight the need for collaborative approaches to AI usage among publishers, editors, reviewers, and authors to ensure that this technology is used ethically and productively.
arXiv Detail & Related papers (2024-06-10T04:10:18Z) - Augmenting the Author: Exploring the Potential of AI Collaboration in Academic Writing [25.572926673827165]
This case study highlights the importance of prompt design, output analysis, and recognizing the AI's limitations to ensure responsible and effective AI integration in scholarly work.
The paper contributes to the field of Human-Computer Interaction by exploring effective prompt strategies and providing a comparative analysis of Gen AI models.
arXiv Detail & Related papers (2024-04-23T19:06:39Z) - Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
Next several decades may well be a turning point for humanity, comparable to the industrial revolution.
Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts.
We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
arXiv Detail & Related papers (2024-04-06T22:18:31Z) - Prompting the E-Brushes: Users as Authors in Generative AI [0.0]
The Copyright Office, in its March 2023 Guidance, argues against users of Generative AI being eligible for copyright protection.
This Article challenges this viewpoint and advocates for the recognition of Generative AI users who incorporate these tools into their creative endeavors.
Rather than dismissing the contributions generated by AI, this Article suggests a simplified and streamlined registration process.
arXiv Detail & Related papers (2024-03-25T02:20:14Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Is AI Changing the Rules of Academic Misconduct? An In-depth Look at
Students' Perceptions of 'AI-giarism' [0.0]
This study explores students' perceptions of AI-giarism, an emergent form of academic dishonesty involving AI and plagiarism.
The findings portray a complex landscape of understanding, with clear disapproval for direct AI content generation.
The study provides pivotal insights for academia, policy-making, and the broader integration of AI technology in education.
arXiv Detail & Related papers (2023-06-06T02:22:08Z) - 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) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Competency Model Approach to AI Literacy: Research-based Path from
Initial Framework to Model [0.0]
Research on AI Literacy could lead to an effective and practical platform for developing these skills.
We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education.
arXiv Detail & Related papers (2021-08-12T15:42:32Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z)
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.