## 6 Discussion
### AHP Analysis of Tensor Relation \"Competency-Content-Module\"  
As was shown, AHP can be used for training course design in competency-based education by helping to prioritize and make decisions regarding various aspects of the course. Applying AHP in this context helped us to present the requirements for the course design in a more concise and clear form. When defining the goal, we establish the ultimate goal of the training course. This could be developing specific competencies in AAI. When identifying criteria that contribute to the achievement of a goal in competency-based education, these criteria could include factors such as the relevance of the content to the desired competencies, the clarity and effectiveness of instructional methods, the assessment strategies used to measure competency attainment, and alignment with industry standards or job requirements. With the help of generated alternatives, we can determine the different alternatives or options for designing a training course on AAI. These alternatives could include different instructional approaches, teaching methodologies, assessment methods, sequencing of topics, course durations, or incorporations of practical exercises or real cases. By applying AHP in the AAI training course design, we can systematically evaluate and prioritize different factors, consider multiple perspectives, and make more informed decisions that align with the goals and requirements of CBE. 6.5.1 Priorities of Competencies from the Point of View of AI/ML  
The analytic hierarchy process (AHP) method can be applied to determine priorities among the competencies related to AI and ML in the context of the job market, academics, students, and employers as decision makers. AHP is a structured approach that helps decision makers compare and prioritize different criteria based on their relative importance. Using the AHP method, decision makers can systematically evaluate and assign weights to competencies. The results from Figures 9-13 can then be used to guide decision-making processes related to AI and ML competencies, such as curriculum development, hiring decisions, or educational programs, taking into account the perspectives of job market representatives, academics, students, and employers. 6.5.2 Priorities of Programming Languages for an AAI Training Course  
Different programming languages have varying support for AI libraries and frameworks, such as Python with TensorFlow and PyTorch, which greatly simplify AI development. Languages like Python and R have vibrant AI communities, offering abundant resources for learning and troubleshooting. Some languages offer flexibility and expressiveness, like Python's simplicity and readability, while others, like Lisp or Haskell, have built-in support for functional programming, which is advantageous for certain AI techniques. The choice of a programming language may depend on performance requirements, with languages like C++ or Java offering better performance than interpreted languages. Integration and deployment needs can also influence the choice of a language, such as using JavaScript for web applications. Python's popularity in the AI community makes it widely used in industry, academia, and research. It is important to note that the specific priorities of programming languages related to AI training courses can vary, depending on the context, technological advancements, and the evolving needs of the job market and employers (see Figures 15-18). Therefore, the most up-to-date and comprehensive results would be obtained from recent studies or surveys conducted by AI experts, research institutions, or industry organizations that specialize in AI and programming languages. The criteria that are important for assessing programming languages' suitability for AI training courses could include factors such as performance, scalability, community support, libraries and frameworks, ease of use, ecosystem, industry adoption, and compatibility with dedicated-server and cloud solutions.