# Artificial Intelligence in Aviation: New Professionals for New Technologies
## 5 Discussion
Explaining and justifying AI model behaviors and decisions. Monitoring AI for accuracy, errors, bias, and unintended consequences. Developing these collaboration abilities requires curriculum activities and simulations working jointly with AI systems. While identifying the AI competencies needed in aviation education is crucial, equally important is laying out actionable steps to integrate these competencies into aviation curricula. A detailed roadmap can provide guidance to put the methodology into practice in order to achieve the desired workforce development outcomes. A roadmap that breaks down the methodology into discrete, sequential stages empowers stakeholders to undertake the multi-faceted process of curriculum reform [67]. By clearly delineating activities like forming expert teams, collecting aviation AI adoption data, holding industry workshops, mapping competencies, and formulating recommendations, aviation educators can progress through the methodology in a structured manner. The roadmap approach lays the groundwork for iterating on the methodology over time. As new technologies emerge, the process can be repeated to continuously refresh competencies. By maintaining the roadmap structure, future competency identification initiatives can build on existing foundations in a consistent fashion. A roadmap for implementing an aviation AI competency methodology could include the following step-by-step procedures (Figure 7):  
1. Form a cross-functional research team. Assemble a team that includes aviation educators, AI experts, industry representatives, and education policymakers. This facilitates collaboration and buy-in. 2. Select focus aviation domains. The team selects which professional areas and occupations to focus the competency analysis on based on the maturity of AI adoption. 3. Gather data on AI adoption. Conduct an environmental scan to collect data on current and emerging AI applications across the focus aviation domains. 4. Hold industry workshops. Host workshops with aviation organizations to identify examples of AI integration and resulting competency requirements. 5. Map core aviation competencies. Analyze focus aviation professions to identify traditional education requirements and learning outcomes. 6. Identify new AI knowledge needs. Determine essential conceptual AI competencies professionals will require specific to their role. 7. Identify new applied AI skills. Determine key applied skills needed to collaborate and work alongside AI technologies in each domain. 8. Develop AI competency framework. Organize identified competencies into a structured framework mapped to an established digital competence model. 9. Collect aviation program data. Gather syllabi, learning outcomes, and course information from existing aviation programs. 10. Compare programs vs. frameworks. Analyze programs to identify gaps between current and needed AI competencies. 11. Formulate recommendations. Develop specific recommendations to address competency gaps through curricular updates and new courses. 12. Share recommendations. Disseminate the competency framework and implementation roadmap to aviation education institutions and accreditation bodies. 13. Track adoption. Establish mechanisms to monitor uptake and iterate on the framework over time to align with evolving technologies. By building aviation education programs that cultivate this combination of conceptual, technical, and collaborative competencies, universities can equip graduates to operate at the forefront of intelligent aviation systems and harness the benefits of AI responsibly and effectively. In Table 6, there are some examples of how AI-based competencies could be integrated into professional aviation courses in European bachelor's programs. Both foundational and specialized AI courses could be offered, integrated into traditional aviation engineering topics, or as dedicated AI programs. The focus should be on experiential learning using real-world data.