Harnessing Generative AI in Academic Publishing

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Key Features

Supports researchers with guidance on responsible and effective use of Generative AI in academic writing and publishing to maximise its benefits

Includes a workbook with keypoints, activities to reinforce the content, and additional online resources

Interactive workshop where the trainer stimulates engagement and discussion with the attendees

Format

1-day live workshop delivered by an expert trainer 

Auditorium or classroom-style 

Available in-person or virtually 

Audience

Up to 250 attendees 

Tailored for early career researchers. Content can be adapted for specific disciplines to be more appropriate for the expertise of the participants. 

Masterclass in Harnessing Generative AI

Generative AI is transforming the way that researchers write, publish, and communicate their studies to the field, improving both efficiency and productivity. However, many researchers are not receiving proper training on how to use generative AI responsibly and effectively. This 1-day workshop discusses important information about various generative AI models, their strengths and limitations, and editorial policies researchers should be aware of. We also showcase various ways generative AI can improve productivity in academic writing and publishing, science communication, academic presentations and grant writing. Nature Masterclasses workshops are available in-person and virtually.

Harnessing Generative AI Workshop – led by experts

1. Generative AI fundamentals

  • Introduces generative AI and how it generates output based on statistical probability and inherent randomness, and how these parameters can lead to creativity but also hallucinations 
  • Compares different architectures, such as dense transformers and mixture-of-experts, along with the strengths and limitations
  • Discusses the importance of open-weight models and show how to download and run these models locally on personal computers for data privacy and security

2. Responsible use of generative AI

  • Discusses some of the limitations that researchers should be aware of when using generative AI, such as hallucinations, biases, and copyright concerns
  • Reviews some of the common concerns related to generative AI and research integrity, such as data manipulation, plagiarism, and paper mills
  • Covers editorial policies about using generative AI in manuscript preparation, authorship, and peer review

3. Prompt engineering

  • Establishes why prompts are so important for generative AI and highlights how to create and use prompts effectively
  • Discusses the factors that makes prompts more effective, such as clarity, precision, and context, along with iteration and refinement
  • Covers commonly used prompt engineering strategies including role play/persona, recipe pattern, cognitive verifier, question refinement, one-/few-shot prompting, and tree of thought prompts

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4. Generative AI in academic publishing

  • Discusses various use cases of generative AI (particularly LLMs) in the preparation and publication of a manuscript
  • Reviews ways generative AI can be used to synthesize and review the literature, using deep research options of common LLMs or AI-driven tools
  • Recommends responsible ways to use LLMs to improve the clarity and readability of a manuscript prior to submission
  • Discusses how LLMs can be used to identify and evaluate journals, improve cover letters, or respond to reviewer comments

5. Other uses of generative AI in academia

  • Introduces other ways generative AI can be used to support a researcher’s journey in academia
  • Illustrates how LLMs can be used for brainstorming new ideas and formulating research questions and aims, along with assessing their feasibility, when preparing grant proposals
  • Demonstrates how LLMs can help amplify the impact of a published study by helping to write engaging social media posts to promote the article
  • Shows how generative AI can be used to help prepare and refine academic presentations before presenting the study at a conference


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