Ajami, MahmoudMahmoudAjami2026-09-172026-09-1720262026 Workshop and Doctoral Consortium at ICCBR, ICCBR-WS-DC 2026https://hdl.handle.net/11420/64924This research investigates the development of an AI Copilot to support experimental scientists and staff during operations at the European X-Ray Free Electron Laser facility as part of ongoing efforts to leverage generative AI in scientific facility operations. The work focuses on problems commonly encountered during operations, identified through an initial survey of scientists and technical staff. The proposed Copilot combines anomaly detection methods with a retrieval-augmented generation (RAG) based knowledge assistant integrated into an open-source messaging platform, enabling scientists to interact with the system through a familiar interface while receiving notifications when anomalies are detected. Control system data collected during operational activities is used to build and evaluate the anomaly detection approach. In particular, a Markov chain-based anomaly detection method is evaluated using a synthetic dataset and later real operational control system data. This work lays the foundation for investigating how an AI copilot can best support European XFEL practitioners, how explainable AI methods based on case-based reasoning can explain anomaly detection outcomes, and how a RAG-based architecture can present real-time anomaly alerts and their explanations in an understandable, actionable, and factually grounded manner while minimizing hallucinations.enAI CopilotAnomaly DetectionEuropean XFELGenerative AIComputer Science, Information and General Works::005: Computer Programming, Programs, Data and SecurityComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceConference Paperhttps://ceur-ws.org/Vol-4255/mahmoud.pdf