The ongoing innovation in the field of computer science naturally also affects the scientific Conceptual Modeling community and presents it with new challenges. This is particularly true for developments in generative AI with some representatives even believing that conceptual modeling will become superfluous in the future.

The FCM workshop aims to address this challenge and, in discussion with representatives of generative AI, to develop the position and benefits of conceptual modeling, including its immense body of knowledge. It is a continuation of the FCM2025 workshop, which was successfully held as part of ER2025 in Poitiers.

Large Language Models (LLMs) and related technologies, such as AI agents, have attracted significant attention in both academia and practice in recent years. Conceptual modeling is no exception: early LLM-based approaches for generating, analyzing, and even executing conceptual models have already demonstrated promising potential. Nevertheless, research at the intersection of generative AI and conceptual modeling is still in its infancy, giving rise to a range of fundamental open questions: How can LLMs and related technologies be effectively integrated across the entire conceptual modeling lifecycle? How will LLMs reshape modeling practices, methods, and tools? In what ways will the tasks and competencies of modelers evolve? Will LLMs act as a catalyst that strengthens the role of conceptual modeling, or could they ultimately challenge and diminish its relevance?

Our growing ability to unravel the complexities of life - from molecular biology to the diagnosis and treatment of diseases - is generating unprecedented amounts of data. This directly impacts the design and future development of information and data management pipelines; thus, new ways of processing data, information, and knowledge in life sciences environments are strongly needed.

The seventh edition of the workshop aims to remain a meeting point for researchers in Artificial Intelligence (AI), Information Systems (IS), Data Management (DM), and Conceptual Modeling (CM) working to develop sophisticated, scalable solutions to the complexity and scale of data generated in life sciences.

From the precise ontological characterization of the components involved in complex biological systems to the modeling of the operational processes and decision support methods used in, for instance, the diagnosis and prevention of diseases, the joined research communities of AI, IS, DM, and CM have an essential role to play; they must help in providing feasible solutions for high-demanding problems of life sciences. CMLS aims to become a forum for discussing the conceptual modeling community's responsibility to support life sciences in light of these new realities.