Techniques for supercharging academic writing with generative AI
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 0 of its 28 references that are in the map. First posted on arXiv 26 Oct 2023.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 5.
9 papers.
- 2025From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychologycited by 9
- 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
- 2024Beyond principlism: Practical strategies for ethical AI use in research practicescited by 7
- 2025Large Language Models as Psychological Simulators: A Methodological Guidecited by 6
- 2025Expert Preference-based Evaluation of Automated Related Work Generationcited by 6
- 2023Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Workcited by 5
- 2024Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writingcited by 3
- 2025GPT Editors, Not Authors: The Stylistic Footprint of LLMs in Academic Preprintscited by 2
- 2024The why, what, and how of AI-based coding in scientific researchcited by 0
- 2025Expert Preference-based Evaluation of Automated Related Work Generation11 Aug 2025
- 2025Large Language Models as Psychological Simulators: A Methodological Guide20 Jun 2025
- 2025From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology20 Jun 2025
- 2025GPT Editors, Not Authors: The Stylistic Footprint of LLMs in Academic Preprints22 May 2025
- 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems3 Mar 2025
- 2024Generative AI Policies under the Microscope: How CS Conferences Are Navigating the New Frontier in Scholarly Writing15 Oct 2024
- 2024The why, what, and how of AI-based coding in scientific research3 Oct 2024
- 2024Beyond principlism: Practical strategies for ethical AI use in research practices27 Jan 2024
- 2023Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work4 Dec 2023