# Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation

arXiv: https://arxiv.org/abs/2608.19299 · HTML: https://mlsanity.com/paper/2608.19299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://mlsanity.com/paper/2211.04054/) (2022)
- [AviationGPT: A Large Language Model for the Aviation Domain](https://mlsanity.com/paper/2311.17686/) (2023)
- [CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management](https://mlsanity.com/paper/2402.14850/) (2024)
- [Automatic Control With Human-Like Reasoning: Exploring Language Model Embodied Air Traffic Agents](https://mlsanity.com/paper/2409.09717/) (2024)
- [Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models](https://mlsanity.com/paper/2605.12332/) (2026)
- [End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent](https://mlsanity.com/paper/2607.06964/) (2026)

## Built on by (most cited first, 1 of 1)

- [V2TATC: Joint Voice-Trajectory Embedding and Dataset for Air Traffic Controller Situational Awareness](https://mlsanity.com/paper/2608.28981/) (2026)

## Abstract (arXiv)

Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management have been semi-automated. In this article, we experimentally evaluate whether large language models (LLMs) can generate operationally realistic ATC transmissions. An experimental general-aviation flight flying over the San Francisco "Bay Tour" route is hand-transcribed and used as ground truth (P0). Through a pilot-in-the-loop process we design five prompt structures (P1-P5) of increasing constraint and embed them in a stateful multi-turn pipeline, where the model plays ATC to a fixed pilot transcript while conditioning on the accumulating dialogue history. Across nine open- and closed-source LLMs we vary the prompt, the presence of a worked transcript from a different experimental flight as an in-context example, and whether the model conditions on its own prior replies or on injected ground-truth history. Turns are scored with lexical, structural, and semantic similarity metrics and by an LLM-as-judge (GPT-5.5) validated against human expert annotation. Supplying a worked example improves similarity, but tightening the prompt does not: the lightest prompts perform best and the most heavily scripted one collapses as its own errors accumulate through the dialogue, which injecting correct history repairs. These results outline a concrete path and its current limits toward LLM-assisted ATC.