{"id":522,"date":"2026-06-24T10:23:55","date_gmt":"2026-06-24T08:23:55","guid":{"rendered":"https:\/\/research.dblue.it\/dialog\/?p=522"},"modified":"2026-06-24T11:41:07","modified_gmt":"2026-06-24T09:41:07","slug":"dialog-open-day-and-workshops-exploring-the-core-questions-on-human-ai-collaboration-in-atm","status":"publish","type":"post","link":"https:\/\/research.dblue.it\/dialog\/2026\/06\/24\/dialog-open-day-and-workshops-exploring-the-core-questions-on-human-ai-collaboration-in-atm\/","title":{"rendered":"DIALOG open day and workshops: exploring the core questions on human-AI collaboration in ATM"},"content":{"rendered":"

On <\/span>9 and 10 June 2026<\/b>, the DIALOG project held its official open day and workshops in <\/span>Rome, Italy<\/b>, marking two intensive days dedicated to research dissemination, technical presentations and strategic discussions. The event brought together the project consortium, led by <\/span>SINTEF (Project Coordinator)<\/b> and comprising the <\/span>Direction des Services de la Navigation A\u00e9rienne (DSNA, French Air Navigation Service Provider, Technical Manager)<\/b>, Deep Blue, the French Aerospace Lab (ONERA), the Deutsche Flugsicherung GmbH (DFS, German Air Navigation Service Provider), and Radboud University, alongside external advisory board members and experts from the German Aerospace Center (DLR), Airudit, commercial airline pilots and the Single European Sky ATM Research Joint Undertaking (SESAR JU). The primary objective of this gathering was to present the project achievements and engage in collaborative workshops to evaluate the progress of<\/span> human-AI collaboration in ATM<\/b>. <\/span><\/p>\n

Day 1: Putting the Teamwork Assistant to the test in a simulated sky<\/b><\/h4>\n

The first day of the event was heavily focused on knowledge sharing and dissemination, while still gathering relevant insights for the overall benefit and impact evaluation of the project from the stakeholder perspective, providing attendees with a deep dive into the broader research scope of <\/span>human-AI collaboration in ATM<\/b>. The consortium presented a live demonstration of the core capabilities of the <\/span>Intent-Inferring Service (IIS)<\/b> and the <\/span>Teamwork Assistant (TA),<\/b> configured to simulate a high-complexity en-route environment based on a sector of the Marseille Air Control Centre (ACC). While a full Teamwork Assistant operationally relies on<\/span> real-time ATCO workload assessments<\/b>, this demonstration focused on showcasing how the system behaves under different simulated workload modes, providing a streamlined yet highly effective presentation for knowledge-sharing purposes. <\/span><\/p>\n

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DIALOG low-fidelity prototype showing tactical conflict detection (TCT) and task load monitoring.<\/span><\/i><\/p>\n

The system operates continuously in the background using two core components: an intent-inferring service, which interprets pilot voice and Controller-Pilot Data Link Communications (CPDLC) requests, and a teammate awareness service, which monitors the controller’s psychophysiological state. During the technical sessions, the partners explained how a dedicated survey was administered to evaluate the cross-national generalisability of the <\/span>Hierarchical Task Analysis (HTA)<\/b> used in the project. This analysis serves as the baseline for determining which tactical subtasks can be successfully automated, directly supporting the project\u2019s goal of targeting <\/span>EASA Level 2B to achieve an effective and adaptive Human-AI Teaming<\/b>. <\/span><\/p>\n

A major point of interest was how the project monitored a controller’s cognitive state seamlessly within an operational environment. The team opted for a non-intrusive suite of <\/span>eye-tracking glasses and hand electrodes<\/b>, ensuring maximum comfort for the operator. This combination of physiological data allowed the system’s algorithm to successfully predict high and low workload states with a <\/span>74% accuracy rate<\/b> during testing.<\/span><\/p>\n

Day 2: What an unexpected simulation glitch revealed about human authority<\/b><\/h4>\n

The second day shifted the focus towards the operational validation results, reviewing qualitative and quantitative data gathered from <\/span>20 simulation runs involving 10 licensed air traffic control officers (ATCOs)<\/b>. While the <\/span>self-report measures<\/span>  showed that controllers found the interface intuitive, describing the assistant as a supportive <\/span>“second pair of eyes”<\/b>, the workshop discussions highlighted a fascinating human-factors lesson that occurred due to an <\/span>unexpected software simulation error<\/b>.<\/span><\/p>\n

During one of the evaluation runs, a technical glitch caused the AI to suggest an incorrect flight <\/span>Flight Level change advisory<\/span> . Rather than disrupting the session, this moment provided the advisory board with concrete proof of the project’s <\/span>safety-by-design concept<\/b>. The controller in the hot seat immediately identified the suboptimal suggestion, overrode the system and <\/span>resolved the aircraft conflict manually<\/b>.<\/span><\/p>\n

This scenario confirmed that the participating controllers <\/span>did not suffer from automation bias<\/b> (the tendency to trust automated suggestions blindly). They maintained full situational awareness and treated the TA as an aid rather than an absolute authority, proving that <\/span>preserving ultimate human command<\/b> acts as the most effective final safety barrier in advanced aviation systems.<\/span><\/p>\n

Addressing the core research questions of human-AI collaboration in ATM<\/b><\/h4>\n

The concluding workshops allowed the consortium and external experts to address several critical research questions that offer practical utility for the wider aviation community, moving past project rhetoric to focus on operational realities:<\/span><\/p>\n