European Guidelines for Use of LLMs in Research

The European Commission has put forward a set of guidelines to support the European research community in their responsible use of generative artificial intelligence (AI). As the adoption of generative AI technology continues to expand across various fields, including science, these recommendations address both the promising opportunities and potential challenges associated with its proliferation. Rooted […]

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Generation Next: Experimentation with AI

We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding experiments, and producing documentation. Second, we discuss the implementation of experiments using LLMs, focusing on enhancing causal […]

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Recognizing and Utilizing Novel Research Opportunities with Artificial Intelligence

As we are witnessing a fundamental transformation of organizations, societies, and economies through the rapid growth of data and development of digital technology (George, Osinga, Lavie, & Scott, 2016), artificial intelligence (AI) has the potential to transform the management field. With the power to automatize, provide predictions of outcomes, and discover patterns in massive amounts […]

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Friend or Foe? Exploring the Implications of Large Language Models on the Science System

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 […]

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Qualitative deductive coding with LLM

Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when working with large datasets. While recent AI-based tools demonstrate utility, researchers may not have readily available AI resources and expertise, let alone be challenged by the limited generalizability of those task-specific […]

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