Introduction théorique et pratique sur les bases de l'IA et applications simples

Y. Seibt and B. Galimberti examine the integration of artificial intelligence into archival practices, illustrating how large language models (LLMs), chatbots, and other AI technologies are redefining the field of archives. Concrete applications include automated transcription and AI-assisted indexing, which help reduce the need for advanced technical skills among archivists. By using prompts, these AI tools provide a simplified and accessible interface, facilitating task automation in Excel or Python for data processing without requiring in-depth expertise in these software tools. Prompts become a strategic vector, allowing archivists to translate specific needs into precise, actionable instructions.

This approach positions AI as a lever to bridge the technological gap and optimize archiving processes while fostering a dynamic of continuous learning. However, it also calls for critical reflection on the limitations and inherent biases of AI models. The objective is to find a balance between the efficiency of automation and the nuance of human interpretation, positioning AI not only as a task executor but also as a pedagogical tool contributing to the development of coding and analytical skills within the archival profession.