​Twenty-one AIR related master programs were surveyed. The programs are from diverse countries and universities, and are scattered among wide geographical areas. Specifically, we surveyed three programs from USA, two programs from Asia, and sixteen programs from Europe. Among the sixteen programs from Europe, there were 10 programs from universities of Program Countries in the consortium. 

The process of analyzing the surveys to get the final recommendations has some difficulties like: same courses may have different names in different universities, different course names may refer to the same course, and the numbers and periods of the courses are different among different universities, different rules in each university, and different assumptions and policies as well. However, the following recommendations are advised to be considered when designing the new master program in UJ and updating existing master programs in universities of Partner Countries:

1) Most master programs are better to focus on one or two areas at most (AI, Robotics, and/or Data science). 

2) AI is needed in data science and robotics master programs. Therefore, master programs, whether in data science or in robotics, usually require one or more courses in AI. 

3) In the master programs which focus on two disciplines like in data science and AI, or master in AI and robotics, the students are required to register one to two core courses in each discipline. 

4) For programs that have two disciplines, the students are better to study set of courses from one group out of two or more groups of courses. For example, the student who is enrolled in a master of AI and robotics, if he/she likes to focus more on AI, at least x courses from a set of AI courses must be studied. The same thing is applied on the student who studies master in AI and robotics and who would like to focus on robotics. 

5) The surveyed master programs divide the courses into mandatory and electives courses. 

6) It is highly recommended that the master programs to be thesis-based or project-based in order to maximize the benefits and expose students to hands-on experience. 

7) It is better to have a big variety of elective courses and small set of mandatory courses. The elective courses fulfil the needs of different students who work in different areas. 

8) The master programs which focus on AI may consider having the following structure:

a. Mandatory courses: Machine Learning, Deep Learning, Applied Machine Learning or machine learning programming, and Intelligent agents.

b. Elective courses: advanced machine learning, computational vision, natural language processing, speech processing and recognition, deep reinforcement learning, and machine learning for data science  

9) The master programs which focus on Robotics may consider having the following structure:

a. Mandatory courses: Introduction to Robotics, Machine Learning, Computer Vision, and Robot Perception and Learning.  

b. Elective courses: Intelligent Systems, Mechanics of Robots, Basics of Mobile Robotics, Human-Robot Interaction, and System Theory and Control Theory. 

10) The master programs which focus on data science may consider having the following structure:

a. Mandatory courses: Database related course, Data mining, Machine learning, and Introduction to Data science.

b. Elective courses: Advanced Data Mining, Large-scale computing, Optimization methods, Multimedia information retrieval and computer vision Social media mining, Data visualization, and Internet of things

11) AI and data science master programs do not need teaching labs other than a computer lab with high specifications computers and powerful graphical processing units. 

12) Robotics master programs need robotics lab that allow the students to do their own testing and experiments.