## 7 Conclusions  
**Overall. ** This study successfully developed a comprehensive approach to designing a training course on AAI in the higher education context. This approach derives from evidence-based pedagogical approaches, and it follows the principles of competency-based education and innovative pedagogy. This research conducted a thorough review of AAI through keyword clustering, incorporating data from surveys, job offers, existing AI training courses, scientific projects, and real cases. The analysis of textual information using word clouds provided valuable insights. The study employed a tensor-based approach to present a competency-based AAI course, ensuring a holistic representation of competencies in relation to the course content and educational modules. This approach allows for a structured and comprehensive understanding of the required skills and knowledge of AAI. Through the solution of decision-making problems using the AHP technique, this research identified specific numerical requirements and priorities for the course, further enhancing the course's design and effectiveness. The findings of this study emphasize the importance of aligning a training course with the needs and preferences of the target audience. By incorporating insights from surveys, educational experience, scientific projects, and business requirements, the developed training course on applied artificial intelligence ensures the achievement of desired competencies and learning outcomes. The implications of this research are significant for higher education institutions seeking to design effective training courses on AAI. The approach presented in this study provides a framework for developing courses that meet the demands of the rapidly evolving field of artificial intelligence while also catering to the specific needs of students and industry requirements. **Research limitations. ** Nowadays, the field of AI is developing actively. That is why the most problematic concern is covering an entire spectrum of AI models, focusing on low-level architecture on the level of particular layers. Moreover, the complete presentation of the models assumes that students possess advanced knowledge and skills in applied math and computer science. So, the proposed design of an AAI course focuses, indeed, on a high level of model architecture, including counterparts like an encoder, a decoder, a transformer, and so on. Also, the AAI course omits the complicated aspects of fitting parameters that influence the model's performance a lot. We assumed the preliminary knowledge of the problems coming from basic courses on AI, like bursting and exploding gradients, overfitting, etc. The limitations of the course design also concern the areas of the application of AI. It is impossible to cover the whole set of branches. So, we focused on the most widely used ones. Thus, we proposed including modules with general titles like \"AI for healthcare\", and we omitted the more specific but no less significant \"AI for genomics\". **Future plans. ** In addition to overcoming the problems originating from the experience with the given AAI course design, we should bear in mind the up-to-date problems of AI deployment. Currently, a fairly serious problem in the development of AI is the limitation of efficiency and the need for computing power, so among the future development trends that should be reflected in the AAI course, we can point to the need to develop deep learning methods that operate on the principle of zero waste and high energy efficiency. In addition, following development trends, we can distinguish between the integration of advanced solutions in the field of AI for robotics, the use of AI in education through the use of appropriate platforms, and the development of multimodal models to which context will also be added from images and sounds. Overall, this work contributes to the advancement of evidence-based pedagogical approaches in the field of artificial intelligence education, specifically in the context of higher education.