This study explores the application of Large Language Models (LLMs) for eliciting tacit knowledge, a challenging area crucial for enhancing decision-making and innovation in organizations. Using a systematic literature review based on PRISMA workflow, the research assesses the potential of LLMs to bridge the gap between tacit knowledge and its articulation. Findings reveal that LLMs, with their advanced natural language processing capabilities, are suitable for capturing tacit knowledge that is typically inaccessible through traditional methods. This paper concludes that while LLMs hold potential for revolutionizing knowledge elicitation practices, careful consideration of their limitations and ethical use is essential. This research contributes to broader discussions on integrating AI in knowledge management and future directions to optimize LLMs’ utility in practical settings.

The swift growth of artificial intelligence has led to the development of large language models, revolutionising various scientific domains and professional fields. This research explores collaborative, cooperative, and competitive game designs, that enhance knowledge elicitation using LLMs in games with a purpose. Through a systematic literature review using the PRISMA workflow, we identify that collaborative and cooperative game designs are more effective for knowledge elicitation than competitive designs. We also address the potential and limitations of incorporating LLMs into GWAPs, such as their use as Non-Player Characters to create engaging interactions. This study provides design principles for GWAPs leveraging LLMs, offering insights for researchers and developers, and discusses ethical considerations and future research directions.

The process of knowledge elicitation is crucial to the field of artificial intelligence because of the lack of data on commonsense knowledge. This paper explores the potential of using large language models (LLM) to enhance knowledge elicitation in games with a purpose (GWAP). By analyzing the capability of LLMs to play games and generate game content, this research shows how LLMs can increase accessibility, efficiency and engagement in GWAPs for knowledge elicitation. The findings show that LLMs can play games with great performance and show much promise in generating game content that facilitates knowledge elicitation. Through a comprehensive literature survey, this research highlights potential ways of enhancing knowledge elicitation in GWAPs using LLMs and offers recommendations for future research in this field.

Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches: explanations requiring further interpretation, non-standardised explanatory format, and overall fragility. In light of this fragmentation, we turn to the field of philosophy of science to understand what constitutes a good explanation, that is, a generalisation that covers both the actual outcome and, possibly multiple, counterfactual outcomes. Inspired by this, we propose CHIME: a human-in-the-loop, post-hoc approach focused on creating such explanations by establishing the causal features in the input. We first elicit people's cognitive abilities to understand what parts of the input the model might be attending to. Then, through Causal Discovery we uncover the underlying causal graph relating the different concepts. Finally, with such a structure, we compute the causal effects different concepts have towards a model's outcome. We evaluate the Fidelity, Coherence, and Accuracy of the explanations obtained with CHIME with respect to two state-of-the-art Computer Vision models trained on real-world image data sets. We found evidence that the explanations reflect the causal concepts tied to a model's prediction, both in terms of causal strength and accuracy.@en