| Title | GenAI and knowledge generation in ATM: Current research and anticipated prospects |
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| Authors | Gurtner, G., Cook, A.J. and Tanner, G. |
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| Type | Conference paper |
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| Abstract | This paper explores the role of generative AI for knowledge generation in air traffic management. It includes a high-level SWOT analysis, a review of the general state of the art and within SESAR. The paper presents the key results of a case study: a trend analysis to build a future concepts roadmap for ATM. It discusses the role of agentic AI and retrieval-augmented generation, both as a follow-up to the roadmap development and to illustrate broader use-case potential in SESAR’s transversal industrial research. |
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| Keywords | Generative AI |
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| GenAI |
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| agentic AI |
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| RAG |
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| SWOT analysis |
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| trend analysis |
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| concepts roadmap |
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| Year | 2025 |
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| Conference | 15th SESAR Innovation Days |
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| Publisher | SESAR |
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| Accepted author manuscript | File Access Level Open (open metadata and files) |
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| Publisher's version | File Access Level Open (open metadata and files) |
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| Publication dates |
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| Published | 01 Dec 2025 |
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| Project | Engage 2 (The SESAR 3 Knowledge Transfer Network) |
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| Funder | SESAR 3 Joint Undertaking under European Union’s Horizon Europe research and innovation programme |
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| UKRI (UK Research and Innovation) |
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| Digital Object Identifier (DOI) | https://doi.org/10.61009/SID.2025.1.56 |
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| Web address (URL) of conference proceedings | https://www.sesarju.eu/SIDS2025 |
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| Web address (URL) | https://www.sesarju.eu/SIDS2025 |
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