🤖 AI Summary
This study addresses the labor-intensive nature of academic writing, where efficiency and quality are often difficult to reconcile. Methodologically, it proposes a human–AI collaborative framework grounded in a three-dimensional “motivation–process–property” coordination model, implements a two-stage human–AI co-writing workflow, establishes a multi-level assistance taxonomy, and systematically integrates prompt engineering with discipline-specific academic norms. Its key contribution lies in empirically uncovering the dual cognitive mechanisms of large language models (LLMs) in scholarly writing: cognitive offloading (e.g., outline generation, draft composition) and imagination elicitation (e.g., structural refinement, stylistic enhancement). Empirical evaluation demonstrates that the framework significantly improves writing efficiency and textual quality, ensures compliance with journal policies, reduces interdisciplinary communication overhead, accelerates scientific dissemination, and supports epistemic diversity in research.
📝 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.