"Papers Aren't Written" Book Giveaway: Soliciting Comments on the Boundary Between AI Assistance and AI Ghostwriting

Event Background

"Writing with AI" is not the same as "letting AI write for you." Against this backdrop, the book *Papers Are Not Written: AI Research Thinking Training and Intelligent Collaborative Writing in Practice* has emerged. The book returns to the underlying logic of research and builds a systematic path of "capability—method—AI collaboration" to help produce truly high-quality academic papers.

Event Schedule

Comment collection period: September 1–4, 2026

Deadline for counting comment likes: 20:00 on September 4, 2026. Winners announced: September 7, 2026 (via WeChat official account post)

How to Participate

Follow the "Data Elements Headline" WeChat official account.

2. In the comment section of this event's post, share your views and insights on any of the following topics:

The Chinese Academy of Sciences explicitly states in its *Integrity Reminder* that AI may be used to assist in organizing theories, materials, and methods, and for language polishing, translation, and standardization checks; however, it opposes treating AI-generated content as core innovative results. Many journals also stipulate that core content such as research design, theoretical viewpoints, and argumentation logic should be completed independently by the authors. In your research practice, how should the boundary between AI assistance and AI ghostwriting be defined? What personal experiences or confusions do you have?

Many academic journals now explicitly stipulate that manuscripts which fail to disclose AI use as required, or where AI-generated content seriously undermines the originality of the paper, will be directly rejected. Do you think journals' strict regulation of AI use is a "necessary measure" or "over-precaution"? As an author, how do you view this trend?

The book *Papers Are Not Written* emphasizes that the core of a high-quality paper lies not in the techniques of "writing" but in research thinking itself. Today, when AI can efficiently complete literature review, language polishing, and even data analysis, what do you think is the most irreplaceable core capability of researchers? Is it critical thinking, problem awareness, or something else?

AI tools are increasingly widely used in research, but improper use may also raise academic integrity risks. Have you ever fallen into a "pit" when using AI to assist your research (such as generating fake references or distorted data)? Or are there any efficient uses that make you feel "this is great"? You are welcome to share your real experiences and lessons.

From literature search to data analysis, from drafting to language polishing, AI is permeating every stage of academic writing. Looking ahead five years, how do you think AI will reshape the ecosystem of academic writing? How should journals, authors, and reviewers each respond to this transformation?

Selection Rules

1. The editorial office will sort all eligible comments by number of likes from highest to lowest;

2. The top 5 comments by number of likes will be the winners;

3. If likes are tied, the comment posted earlier takes priority;

4. Each participant may participate with only one WeChat account; for repeated comments, only the one with the most likes will count.

Prize

One physical copy of *Papers Are Not Written: AI Research Thinking Training and Intelligent Collaborative Writing in Practice* (authors: Chen Zhe, Li Yixin; Publishing House of Electronics Industry; ISBN: 978-7-121-52953-5).