## 5 Results of the Survey
### Results Related to AI
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**To What Extent Do You** & & & & & & & \\
**Teach the Following** & & & & & & & \\
**AI Areas?** & & & & & & & \\ \hline Machine Learning & 2.9\% & 10.1\% & 15.9\% & 20.3\% & **26.1\%** & 23.2\% & 1.4\% \\ Deep Learning & **21.7\%** & 18.8\% & 8.7\% & 17.4\% & 17.4\% & 14.5\% & 1.4\% \\ Data mining & 11.6\% & **20.3\%** & 18.8\% & 15.9\% & **20.3\%** & 8.7\% & 4.3\% \\ Computational intelligence & 13.0\% & 17.4\% & 15.9\% & **20.3\%** & 15.9\% & 11.6\% & 5.8\% \\ Natural language processing & **43.5\%** & 18.8\% & 14.5\% & 11.6\% & 4.3\% & 2.9\% & 4.3\% \\ Computer vision & **39.1\%** & 13.0\% & 15.9\% & 10.1\% & 11.6\% & 8.7\% & 1.4\% \\ Cognitive computing & **42.0\%** & 24.6\% & 17.4\% & 8.7\% & 1.4\% & 1.4\% & 4.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Areas of AI in teaching, 100% = 69. Table 9 contains answers to the question concerning three main techniques of computational intelligence (CI). Data show that teaching of CI focuses mainly on neural networks. Fuzzy systems and genetic algorithms make a more negligible contribution to teaching programs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**To What Extent Do You Teach** & \multirow{2}{*}{**Not at All**} & **To a Small** & **To Some** & **To a** & **To a Great** & **To a Very** & **No** \\
**Natural Language Processing** & & **Small** & **Extent** & **Accent** & **Accent** & **Accent** & **Accent** & **Accent** & **Answer** \\ \hline Speech recognition & **62.3\%** & 11.6\% & 10.1\% & 8.7\% & 1.4\% & 2.9\% & 2.9\% \\ Natural language generation & **66.7\%** & 13.0\% & 7.2\% & 4.3\% & 4.3\% & 1.4\% & 2.9\% \\ Natural language translation & **68.1\%** & 14.5\% & 7.2\% & 2.9\% & 2.9\% & 1.4\% & 2.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Natural language processing issues, 100% = 69. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**To What Extent Do You Teach** & \multirow{2}{*}{**Not at All**} & **To a Small** & **To Some** & **To a** & **To a Great** & **To a Very** & **No** \\
**Mining Phases** & & **Extent** & **Extent** & **Accent** & **Extent** & **Extent** & **Accent** & **Answer** \\ \hline \hline \ \begin{tabular}{c} Imaging \\ Data understanding \\ Data preparation \\ Modeling \\ Evaluation \\ Development \\ \end{tabular}  &
\begin{tabular}{c} **34.8\%** \\ **40.6\%** \\ **40.6\%** \\ **44.9\%** \\ **44.9  
Other mentioned programming languages used in AI teaching are: (1) mentioned once--Agent-Oriented programming languages (AgentSpeak, DALI), Julia, CLIPS, IDEF, UML, Scala, SQL, Kotlin (Android); (2) mentioned twice--C#, Javascript. Table 14 shows that academics use no universal software package to teach AI. Other mentioned programming languages are: Apache Spark, BPMN, DTREG Predictive Modeling Software, EXSYS Professional, Google Colaboratory, IBM SPSS Modeler, Infor Coleman, Intellij IDE, Jade, Jupyter, Keras, Knowledge Extraction based on Evolutionary Learning, Minitab, Natural Language Toolkit, Netbeans, Node.js, NumPy, NY Dataset, Pandas, PostgreSQL, Protege, Pytorch, Rapidminer, Robot Operating System, Scikit-learn, Self-written programs and algorithms, Sicstus Prolog, Spade, Tensorflow, Unity, Visual Studio, Waikato Environment for Knowledge Analysis, YOLO: Real-Time Object Detection. We can see that teachers work with standard programming environments (Intellij, Netbeans), specialized AI systems (YOLO, IBM SPSS Modeler, Tensorflow), or write their software. Teachers use varieties of programming languages and environments. According to Table 14, MS Excel is a widely used tool. Optimization, computer vision, and anomaly detection are the leading AI applications that are taught by academics, as shown in Table 15. Analyzing the mentioned table, we can say that the least discussed issues are deliveries, cognitive systems, and supply chain management.