This study explores the design of two explanation formats for a conceptual AI-based assistant in low-level urban drone traffic management, to support effective Human-AI collaboration. The design solutions aim to present decision alternatives and explanations in time-critical situations, crucial for enhancing situation awareness and understanding of the AI systems. Using research through design, a three-part design process was used to develop two explanation formats, which were then evaluated with three air traffic controllers and three novices. Each participant completed two trials with explanations presented using a text-based or graphic-based format in response to a static scenario involving an unpiloted air taxi transporting a passenger experiencing a medical emergency. After each trial, participants completed the Explanation Satisfaction Scale to evaluate aspects such as understanding, clarity, detail, completeness, and effectiveness. Their answers were then discussed using open-ended questions to derive qualitative insights into their evaluation of the explanations. Results revealed differing preferences: controllers favored the text-based presentation, appreciating its detail, while novices preferred the graphical version, appreciating its ease of processing information. The study concludes that a hybrid approach that combines and leverages respective strengths of text-based and graphical explanations could potentially offer a more versatile solution for explaining AI system behaviors that accommodates a broader range of user experience levels and preferences.