## 3 The Background of the Study
### Scope of Problems and Areas of AAI  
Within the framework of the FAA project, 267 real cases based on AAI solutions were studied. Table 1 shows the areas of the tasks, together with their significance. The scope of artificial intelligence problems is broad and diverse. AI can be applied to a wide range of domains and can address various types of problems. Some areas where AI can be leveraged are as follows: automation, decision support, natural language processing, image and video analysis, healthcare, robotics and autonomous systems, gaming and entertainment, cybersecurity, smart cities, and environmental monitoring. These examples represent just a fraction of the scope of AI problems. AI continues to advance and find applications in various industries and sectors, creating new opportunities for solving complex challenges  
Figure 1: Cluster plot of WoS publications throughout the 10 last years in response to a query concerning applied artificial intelligence.  
and improving efficiency and decision-making processes. A wide variety of real AI cases should be taken into account when designing a competency-based course on AI.  
We especially focused on scientific projects, as they contribute research capabilities to real-world problems and could be directly implemented in higher education. Part of the research in the FAAI Erasmus + program involved a questionnaire about scientific projects in applied AI. The questions were aimed at researching the needs and expectations of scientific projects to aid in training specialists in the field of applied AI. Questionnaire about 63 projects collected by partner organizations from the five participating countries were collected and analyzed. The project coordinators were from 19 countries; 34 came from universities, 6 from academies of science, and 24 from other organizations or companies. Since the questionnaires primarily focused on ongoing projects, the obtained results can be considered current and up-to-date. Most of the projects had 1-6 participants; however, one project had 50 participants.  
Among the more interesting results is the finding that more than half of the projects concerned deep neural network learning modules, and most of the machine learning tasks that were solved involved image processing, classification, regression, clusterization, and natural language processing. Of the 63 questionnaires, 55 were concerned with ML tasks. Knowledge representation and reasoning, planning and search strategies, expert systems, and fuzzy logic were not mentioned in the projects. Most of the cases used open software libraries. Among the AI libraries used, TensorFlow, Keras, scikit-learn, and CUDA were dominant. The programming languages were Python and C++.  
\begin{table}
\begin{tabular}{l c} \hline
**Scope** & **Percentage** \\ \hline healthcare & 17.60\% \\ ecology & 9.74\% \\ cybersecurity & 8.99\% \\ manufacturing & 7.49\% \\ data processing & 5.24\% \\ robotics & 4.87\% \\ smart grid & 3.75\% \\ finance & 3.75\% \\ energetics & 3.75\% \\ recommendation systems & 3.00\% \\ agriculture & 3.00\% \\ photo and video & 2.62\% \\ face and body recognition & 2.25\% \\ culture & 2.25\% \\ chatbots & 2.25\% \\ business intelligence & 2.25\% \\ automotive & 2.25\% \\ voice recognition & 1.87\% \\ video processing & 1.87\% \\ geolocation & 1.87\% \\ education & 1.87\% \\ road traffic & 1.50\% \\ object detection & 1.50\% \\ transport & 1.12\% \\ search and recommendation & 1.12\% \\ library & 0.75\% \\ aviation and ocean transport & 0.75\% \\ social network analytics & 0.37\% \\ military & 0.37\% \\ \hline \end{tabular}
\end{table}
Table 1: The scope of AI problems according to a decreasing order of significance.