Brainstorming Generative AI for Peer Review: Using Narrative and Concise Instructions to Reinforcement Discussion and Correspondence

Generative AI can be a valuable tool in the peer-review process. After reading and analyzing a manuscript, summarize key points and areas for review. Then, use the AI to help organize and articulate your feedback (without directly inputting or uploading the manuscript’s text, thus avoiding privacy concerns). For example, you might instruct the AI: “Assume you’re an expert and seasoned scholar with 20+ years of academic experience in [field]. On the basis of my summary of a paper in [field], where the main focus is on [general topic], provide a detailed review of this paper, in the following order: 1) briefly discuss its core content; 2) identify its limitations; and 3) explain the significance of each limitation in order of importance. Maintain a concise and professional tone throughout.”

Regardless of which generative-AI tool you choose, the key to success lies in providing precise instructions. The clearer you are, the better. For example, you might write: “I’m writing a paper on [topic] for a leading [discipline] academic journal. What I tried to say in the following section is [specific point]. Clarifying it will ensure each paragraph flows into the next. Remove jargon. Use a professional tone. You can use the same technique later on to clarify your responses to reviewer comments.

The first reply might not be perfect, but it is a collaborative process. If you are talking about a concept with a colleague, you might want to add more information to your instructions. It’s the interaction that improves the results. If something doesn’t quite hit the mark, don’t hesitate to say, “This isn’t quite what I meant. Let’s adjust this part.” Or you can commend its improvements: “This is much clearer, but let’s tweak the ending for a stronger transition to the next section.”

For instance, after evaluating a paper and noting its pros and cons, I might feed these into ChatGPT and get it to draft a suitable letter: “On the basis of these notes, draft a letter to the author. The manuscript has an interesting topic but may not provide a substantial enough advancement to merit publication. Avoid jargon. Don’t be hesitant to be direct. The professional and respectful tone should be maintained. It will take a few times to get the tone and content right.

I’ve found that this approach both enhances the quality of my feedback and helps to guarantee that I convey my thoughts supportively. The more positive dialogue between the editors and the authors is a result.

Generative AI isn’t just a tool to find plagiarism: What students really need to know about a language, machine learning, and chatbots

Detecting the use of gen AI is tricky. It’s not as easy as flagging plagiarism, because generated text is still original text. Plus, there’s nuance to how students use gen AI; some may ask chatbots to write their papers for them in large chunks or in full, while others may use the tools as an aid or a brainstorm partner.

Students aren’t interested in only two models, a large language model and a small language model. Word spinners are another type of machine learning software that can be used to rewrite text, and it may make it harder to find out that work was plagiarised. Turnitin’s AI detector has also been updated to detect word spinners, says Annie Chechitelli, the company’s chief product officer. It can also flag work that was rewritten by services like spell checker Grammarly, which now has its own generative AI tool. As familiar software increasingly adds generative AI components, what students can and can’t use becomes more muddled.