AI-Assisted Scientific Writing: From Literature Search to Publication
Dates
3-5 June 2026
To foster international participation, this course will be held online
Course overview
The rapid evolution of large language models (LLMs) and autonomous agents is transforming scientific writing — from literature search and synthesis to drafting, revision, and even peer review. This practical course provides researchers with hands-on experience in applying state-of-the-art AI tools to accelerate and enhance every stage of scientific writing.
Participants will explore the AI landscape, learn effective prompting strategies, and experiment with advanced multi-agent review workflows. Through guided exercises, they will build reproducible and auditable AI workflows, critically evaluate AI-generated content, and draft sections of their own papers using cutting-edge tools.
Target Audience
1. Researchers familiar with basic scientific writing
2. No coding background required
3. Free, browser-based tools (ChatGPT or Claude account recommended)
Learning outcomes
- Accelerate literature review and drafting using state-of-the-art LLMs and autonomous agents
- Design reproducible, auditable AI workflows for scientific writing and peer review
- Critically evaluate AI-generated content for accuracy, ethics, and transparency
Instructor resources provided to participants
- Slide deck summarising AI taxonomy, latest model capabilities and tool-selection heuristics.
- Colab notebooks for the Deep Research and Agentic Paper exercises.
- Pre-course survey template and post-course reference pack.
Session content
Day1 - 1-4 PM Berlin time
Focus: Evolution to transformer era, taxonomy of symbolic AI → generative AI, responsible use, data-privacy checkpoints
Hands-on components & key take-aways: Prompt lab: simple vs. JSON-structured prompts to build a manuscript outline
Day2 1-4 PM Berlin time
Focus: Prompt patterns for coherent sections, integrating domain data & statistics, collaborative workflows; overview of 2025 LLM advances (o3, Claude 4, Gemini 2.5, DeepSeek R1)
Hands-on components & key take-aways: Participants draft a section of their own paper; Deep Research demo – automatic search → reading → synthesis of hundreds of sources in minutes
Day3 1-4 PM Berlin time
Focus: Fact-checking, citation control, hallucination mitigation; multi-agent review concepts (MARG architecture)
Hands-on components & key take-aways: Guided run of Agentic Paper (https://github.com/albertogerli/Agentic_Paper): each attendee launches the pipeline on a short manuscript and analyses the specialised reviewers’ reports
COst overview
what people say about this course
"The breadth of AI tools available far exceed my knowledge."
"I enjoyed this course and have already recommended it. The group environment was stimulating and motivating. As a person that works remotely, it is important to have these learning opportunities where one can connect with other people in similar fields."
Related courses
1 - AI-Powered Python for Bioinformatics - ONLINE, 1-2 July
2- Agentic AI for Life Sciences - ONLINE, 6-7 July
Cancellation Policy:
> 30 days before the start date = 30% cancellation fee
< 30 days before the start date= No Refund.
Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.