• In air traffic management, AI techniques optimize traffic flow, enhance air traffic controllers' situational awareness, and enable more automated coordination of aircraft movements and communications.
• For aviation safety, AI tools analyze flight recorder data, identify emerging risks or anomalies, and assist in investigating incidents and accidents.
• In maintenance, AI enables predictive diagnostics of potential equipment failures, automated visual inspection using computer vision, and ongoing condition monitoring.
• For unmanned aerial vehicles (UAVs), AI increases autonomy in navigation, communications, threat detection, and decision-making.
• Key civil aviation authorities recognize AI’s pivotal role and emphasize the need to build workforce expertise through education and training initiatives.

• A structured literature review of both aviation and AI education sources.
• Analysis of civil aviation authority strategy documents pertaining to AI.
• Focus groups and interviews with aviation educators and industry representatives.

3. Competency Gap in the Aviation Bachelor's Programs

• The aviation industry comprises diverse sectors such as airlines, airports, maintenance, training institutions, and aircraft/engine manufacturers.
• Current aviation curricula cover foundational areas like piloting, engineering, air traffic control, and management.
• AI is rapidly transforming multiple facets of aviation, including flight planning, predictive maintenance, passenger experience enhancement, and training simulations.
• Aviation professionals require new competencies in conceptual AI knowledge, technical AI skills, and human-AI collaboration abilities to work effectively in increasingly AI-driven environments.
• AI competencies can be mapped to established digital competence frameworks like the European Digital Competence Framework (DigComp) to provide structure.
• Analysis of 16 aviation engineering bachelor's programs across Europe indicated a limited focus on building AI literacy and competencies, with more emphasis on foundational digital skills.
• Reasons for this gap include the relative newness of extensive AI adoption in aviation, lack of coordinated competency requirements from industry, and specialized nature of AI skills.
• The proposed competency framework identifies essential conceptual knowledge, technical abilities, and human-AI collaboration skills aviation professionals need in an AI-capable era.

A detailed implementation roadmap was developed to guide the integration of AI competencies into aviation curricula in a methodical, phased approach.

Discussion

The analysis revealed that current aviation bachelor’s programs do not adequately focus on building the AI literacy and competencies increasingly required by the industry as it adopts these technologies. While basic digital skills are incorporated into most curricula, focused instruction in core AI areas like machine learning, data science, and human-AI interaction appears generally lacking but critically necessary.

Several factors likely contribute to this competency gap:

• The cutting-edge nature of extensive AI adoption across aviation means programs are still assessing workforce needs.
• Lack of coordinated competency requirements from industry makes curriculum design challenging.
• Specialized skills required for AI literacy do not fit neatly into traditional aviation engineering curricula.
• Faculty themselves often lack exposure to AI to guide students in these rapidly evolving topics.

As the field continues maturing, rwesearchers may see dedicated bachelor’s programs in aviation-focused AI emerge to produce graduates holistically skilled in this area. But waiting for the technology and workforce needs to fully stabilize risks aviation education lagging behind industry advancements. The proposed framework offers a guide to help programs proactively develop graduates ready to understand and utilize AI tools.

The framework identifies the core conceptual knowledge, technical abilities, and human-AI collaboration skills aviation professionals need to complement AI technologies safely and effectively. It can be integrated through new courses, modules in existing topics, and collaborative initiatives between academia, industry, and government. The implementation roadmap provides guidance to competency analysis, curriculum redesign, and ongoing updating as innovations continue accelerating.

More cross-sector collaboration can help define AI competency requirements for various aviation roles to spur curricular innovation. But forward-looking programs can already use the framework to equip students with capacities aligned with aviation’s digital future.

Conclusion

• The aviation industry comprises diverse sectors such as airlines, airports, maintenance, training institutions, and aircraft/engine manufacturers.
• Current aviation curricula cover foundational areas like piloting, engineering, air traffic control, and management.
• AI is rapidly transforming multiple facets of aviation, including flight planning, predictive maintenance, passenger experience enhancement, and training simulations.
• Aviation professionals require new competencies in conceptual AI knowledge, technical AI skills, and human-AI collaboration abilities to work effectively in increasingly AI-driven environments.
• AI competencies can be mapped to established digital competence frameworks like the European Digital Competence Framework (DigComp) to provide structure.
• Analysis of 16 aviation engineering bachelor's programs across Europe indicated a limited focus on building AI literacy and competencies, with more emphasis on foundational digital skills.
• Reasons for this gap include the relative newness of extensive AI adoption in aviation, lack of coordinated competency requirements from industry, and specialized nature of AI skills.
• The proposed competency framework identifies essential conceptual knowledge, technical abilities, and human-AI collaboration skills aviation professionals need in an AI-capable era.

A detailed implementation roadmap was developed to guide the integration of AI competencies into aviation curricula in a methodical, phased approach.

5. Conclusion

Сurrent aviation bachelor’s programs generally do not focus sufficiently on developing the specialized AI competencies and literacies required as the industry adopts these transformative technologies. While foundational digital skills remain relevant, focused instruction in AI areas like machine learning, data science, and human-AI interaction is urgently needed but still emerging.

Bridging this competency gap will require proactive efforts by aviation educators to analyze industry needs, map required competencies, redesign curricula, upskill faculty through training, and collaborate closely with technology leaders and regulators. Aviation higher education must keep pace with the field’s accelerating digital transformation. Equipping the next generation with relevant knowledge and abilities will allow human expertise to complement aviation AI technologies synergistically rather than be displaced. This will enable the intelligent future of air transportation.

The researchtudy provides an initial conceptual framework and implementation roadmap aimed at guiding aviation training institutions to systematically prepare graduates for this AI-driven era. But realizing the vision will require sustained commitment to research, creativity, cross-sector partnerships, and continuous improvement as both the technologies and resulting competency needs rapidly evolve. This necessitates an agile, lifelong learning mindset from both educators and aviation professionals. With proactive coordination, aviation education can lead the field into an innovative yet human-centric intelligent future.