This paper investigates the role of AI, Multimodal Machine Learning (MML), and Explainable AI (XAI) in enhancing remote digital towers (RDTs) for air traffic management. By focusing on human-AI teaming, human-centred XAI, and interactive interfaces, the study defines functional requirements for taxiway and runway monitoring and decision support within RDT operations. It highlights how transparent, explainable, and actionable AI insights can improve safety, efficiency, and collaboration between air traffic controllers and AI systems. The research combines systematic literature review with practical guidelines for future development of multi-modal intelligent agents in the RDT domain, fostering trust and operational resilience. Published: 09 July 2025
Link/DOI: 10.1145/3719384.3719389