The Impact of AI on Scientific Literature: Navigating Emerging Challenges in the Scientific Information Landscape

Aims

Scientific literature is one of many areas transformed by the widespread use of artificial intelligence (AI) tools. Large language models (LLMs) such as ChatGPT enable an almost effortless production of scientifically formulated texts. This development is likely to transform science communication. Writing scientific publications will be easier and faster. While this is an advantage if used in a meaningful way, it is also likely to enhance existing problems with the quality of scientific publications under the “publish or perish” paradigm. Scientific publications are currently the most important measure for research performance in academia, resulting in problems for scientific literature such as lack of reproducibility, predatory publishers, paper mills, plagiarism or data fraud. The easy availability of powerful AI tools has the potential to exacerbate these problems and complicate the search for reliable and relevant literature. The aim of this workshop is to raise awareness of these new challenges in science communication and to learn targeted approaches for working with scientific literature in the age of AI.

Content

• Overview of AI-related terminology and state of the art: LLMs, machine learning, deep learning, neural networks, generative and statistical models, interpretability etc.
• Current state of scientific literature: is there a reproducibility crisis?
• Scientific information searching: conventional and LLM-powered search engines and scientific databases
• LLMs for scientific writing: current usage and characteristics of AI-generated text
• Data visualization and image generation by AI
• Outlook: transformation of the scientific information landscape by AI

Methods

Presentations
Case-based discussions
Group discussions
Self-study

Target Group

All Doctoral Candidates & Postdocs

Requirements

The workshop is suitable for anyone interested in the transformation of science communication by AI. Prerequisites are to bring a computer.

About the Trainer

Dr. Robin Segerer hold positions as scientific information expert for psychology at the University Libraries of Zürich and Basel. He is an experienced researcher in the field of developmental language psychology, reading literacy, psychometrics, and statistical modeling, ORCID: 0000-0002-9044-3985

Dr. Nuria Plattner is a scientific information expert for natural sciences at the University Library of Basel and has more than ten years of research experience in computational modeling and simulations of biomolecules. ORCID: 0000-0003-4115-6741

Workload

Total of 3.5h (course attendance)

Feature

Once registration is open, applications will be collected for 24 hours and course places allocated by lot. All registrations received after the initial 24h period will be put on a waiting list and assigned on a first come, first served basis.

Course places/places on the waiting list will be confirmed by e-mail. Course registrations can only be canceled before the registration period ends (send an e-mail to grace@unibas.ch). Full course attendance is mandatory. Participants who fail to attend a course without prior notification or withdraw after the registration deadline are subject to a fee of CHF 30. In addition, participants who cancel their course registration at a later point in time, are absent without an excuse or do not attend the entire course will, for reasons of fairness, not be considered for course registration in the following semester and will be removed from other courses offered in the same semester. Please find the detailed regulations on the Transferable Skills Homepage.