# Artificial Intelligence in Aviation: New Professionals for New Technologies
## 5 Discussion  
While Table 5 shows that many aviation and aerospace engineering programs incorporate digital technologies and computer skills, it does not indicate a strong focus on artificial intelligence and machine learning competencies at the bachelor's level. As seen in Table 5, while keywords related to digital technologies appear frequently, explicit mentions of AI and ML topics are rare. Only 9 out of 16 programs' descriptions contained any AI/ML keywords, and they are just generic terms like \"simulation\" and \"automate-\". There are several potential reasons for the limited focus on AI and ML competencies in aviation bachelor's programs. Firstly, AI and ML are still emerging technologies in aviation, so programs may be cautious about altering curricula until the benefits are proven and technical challenges are resolved. Additionally, AI skills like data science do not fit neatly into traditional engineering curricula. Aviation companies are also still evaluating how to integrate AI operationally, so clear workforce requirements have not emerged yet. Furthermore, aviation AI involves advanced applications more common at the graduate level. There are also regulatory and ethical implications still being assessed that give programs pause. Finally, faculty themselves often lack exposure to guide students in these rapidly evolving topics. In summary, aviation bachelor's programs appear slow to adapt due to the cutting-edge nature of AI, lack of industry clarity on needs, the specialized skills required, and faculty themselves needing upskilling in this novel area. More collaboration between academia, industry, and regulators could help define aviation AI competency requirements to spur curricular innovation. While digital technologies are becoming increasingly integrated into aviation engineering programs, full-fledged AI and ML competencies are likely still beyond the scope of most bachelor's level curricula at this point. As the industry and technologies mature, we may see more bachelor's programs emerge that aim to produce \"AI-ready\" aerospace engineers. Based on the analysis of evolving industry needs, the following framework is proposed for integrating essential AI competencies into aviation programs. The framework comprises three primary areas. A. Conceptual AI Knowledge Areas  
To work effectively with AI systems, aviation professionals require foundational knowledge across key AI concepts, including:  
* Machine learning--understanding supervised, unsupervised, and reinforcement learning models. * Neural networks--knowledge of deep learning architectures and training techniques. * Natural language processing--comprehending how NLP systems analyze text and language. * Computer vision--principles of how AI can identify patterns in visual data. * AI ethics--awareness of ethical issues like bias, transparency, and responsibility. Aviation curricula should incorporate modules on each of these topics to cultivate conceptual literacy regarding the techniques and technologies powering modern AI systems. B. Technical Skills and Tool  
Hands-on abilities to build, implement, and apply AI systems are critical. Key skills include:  
* Coding in languages like Python and R to program AI algorithms. * Working with AI development platforms such as TensorFlow and PyTorch. * Data analysis and visualization to prepare, understand, and interpret AI training data. * Using AI APIs and cloud services like AWS AI to integrate smart systems. * Monitoring, maintaining, and retraining AI models in production environments. Aviation programs need to provide lab courses and projects that allow students to actively experiment with AI tools and technologies. C. Human-AI Collaboration Skills  
Since AI will work alongside humans, aviation professionals need abilities like:  
Identifying high-value opportunities for AI augmentation in aviation tasks. Communicating and coordinating effectively with AI agents. Judging when to rely on AI systems vs. human expertise and experience.