Friend or Foe? Exploring the Implications of Large Language Models on the Science System
This research addresses the impact of LLMs on scientific practice, identifying both opportunities and challenges for the science community, though it is incremental as it builds on existing discourse with empirical insights.
The study investigated the implications of large language models (LLMs) on science through a Delphi study with 72 experts, finding transformative potential in tasks like administration and analysis but highlighting risks such as bias and misinformation.
The advent of ChatGPT by OpenAI has prompted extensive discourse on its potential implications for science and higher education. While the impact on education has been a primary focus, there is limited empirical research on the effects of large language models (LLMs) and LLM-based chatbots on science and scientific practice. To investigate this further, we conducted a Delphi study involving 72 experts specialising in research and AI. The study focused on applications and limitations of LLMs, their effects on the science system, ethical and legal considerations, and the required competencies for their effective use. Our findings highlight the transformative potential of LLMs in science, particularly in administrative, creative, and analytical tasks. However, risks related to bias, misinformation, and quality assurance need to be addressed through proactive regulation and science education. This research contributes to informed discussions on the impact of generative AI in science and helps identify areas for future action.