## 6 Discussion  
This section provides an overview of the research results and answers to the research questions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**To What Extent Do You Teach Edge Computing in the Following Applications?** & **Not at All** & \begin{tabular}{c} **To a** \\ **Small** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **To Some** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **To a** \\ **Moderate** \\ **Extent** \\ \end{tabular} & \begin{tabular}{c} **To a** \\ **Great** \\ **Extent** \\ \end{tabular} & \
\begin{tabular}{c} **To a Very** \\ **Great** \\ **Answer** \\ \end{tabular} &
\begin{tabular}{c} **No** \\ **Answer** \\ \end{tabular} \\ \hline Autonomous machines & **50.0\%** & 0.0\% & 25.0\% & 12.5\% & 0.0\% & 12.5\% & 0.0\% \\ Augmented reality & **62.5\%** & 25.0\% & 0.0\% & 0.0\% & 0.0\% & 12.5\% & 0.0\% \\ Mobile agents (e.g., drones) & **62.5\%** & 0.0\% & 0.0\% & 25.0\% & 12.5\% & 0.0\% & 0.0\% \\ Autonomous products & 25.0\% & 25.0\% & **37.5\%** & 0.0\% & 0.0\% & 12.5\% & 0.0\% \\ Autonomy in energy networks & **62.5\%** & 25.0\% & 0.0\% & 12.5\% & 0.0\% & 0.0\% & 0.0\% \\ Facial recognition algorithms & **62.5\%** & 12.5\% & 0.0\% & 0.0\% & 0.0\% & 25.0\% & 0.0\% \\ Smart cities & 25.0\% & 25.0\% & 25.0\% & 0.0\% & 0.0\% & 25.0\% & 0.0\% \\ Industry 4.0 & **37.5\%** & 25.0\% & 0.0\% & 12.5\% & 12.5\% & 12.5\% & 0.0\% \\ Autonomous production & **50.0\%** & 37.5\% & 0.0\% & 0.0\% & 12.5\% & 0.0\% & 0.0\% \\ planning system & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table} 
Table 27: Teaching applications of Edge Computing, 100% = 8. Regarding the first research question (**RQ1**), from the presented survey results (see Figure 6), it can be concluded that AI is the most taught. In 80% of questionnaires, teachers indicated that they teach issues related to AI; 36% of teachers indicated that they teach IoT, and only 9% of teachers indicated that they teach EC. The reason why so few teachers teach EC-related topics may be that edge computing is a new domain of science, as Figure 8 shows (see [57]). Therefore, academics still have not adapted learning programs to the new knowledge. Regarding the second research question (**RQ2**) the research results (see Figure 6) showed that 80% of academics who participated have included AI teaching in their courses, resulting in this scientific field being much more widespread in curricula than IoT and EC. This is also evidenced by the fact that 78% of the total courses are AI-related (Figure 4). The teaching process of AI is based mainly on concepts and principles of computer science, mathematics, and logic (Table 4). In addition, most taught AI subsets are machine learning (mainly supervised and unsupervised learning), deep learning, data mining, and computational intelligence (Table 5). Furthermore, in improving students' practical skills, the most taught programming languages for AI solutions deployment are Python and Matlab (Table 13). At the same time, more attention has been paid to optimization problems, computer vision, and anomaly detection for application areas (Table 15). We have compared the results which we have obtained with other published works. For example, the authors of [58] investigated the courses offered at the undergraduate and postgraduate level by highly-rated schools such as Carnegie Mellon University, Massachusetts Institute of Technology (MIT), and University of California- Berkeley. According to this study, these universities teach an average of 10 AI-related courses, with a greater emphasis on machine learning (three courses per university), computer vision (two courses per university), natural language processing (two courses per university), and robotics (two courses per university). At the same time, each school offers at least one introductory AI course and advanced courses like knowledge representation and automated reasoning.