# Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines
## 3 Artificial Intelligence Techniques for Designing Intelligent Tutor Robots
### Planning of Teaching Contents and Strategies
#### 3.2.2 Teaching Decision Making  
The ITR may interact with the student's model with multiple candidate plans of teaching contents and strategies and make predictions on the outcome of each candidate plan. The next step is to compare these candidate plans and make a decision.  
In general, there is no optimal plan since the solution may serve contradictory objectives in multiple dimensions. For example, the plan may select easier problems for motivating students to enjoy short-term success, while it may lead to smaller learning gains in the long run. As shown in Figure 5, the ITR has to take at least three factors into account, e.g., long-term education objective, short-term curriculum objective, and a student personal objective. The long-term education objectives include facilitating students into self-directed learners as well as equipping students with general learning tools so that the students excel in the life-long learning process. The short-term curriculum objectives usually aim to promote students to master knowledge in the context of the curriculum. The students' personal objectives may be further diversified according to their own interests [23].  
Figure 5: Critic design needs to take into account multiple interrelated objectives and may benefit from human tutor supervision. The critic may select from multiple action proposals before making a decision.  
In many cases, it is a challenge for the ITR to formulate the multiple-objective optimization problems by itself. As shown in Figure 5, human futors and curriculum designers may intervene in and design quantitative and assessable cost functions to facilitate the decision-making process for the ITR. As long as the cost function is properly designed for the criteria, the machine learning techniques may compute the decision actions from multiple action proposals. In Reference [47], the authors propose a selection algorithm for adaptive tests based on multi-criteria decision models integrating expert knowledge by fuzzy linguistic information. In Reference [48], the authors propose a four-stage decision-making plan and a synthesis mechanism for cognitive maps of knowledge diagnosis.  
In general, the decision plan may be interpreted in a form of action sequence to intervene in and impact students' learning [3]. The action sequence is fed to the action modules of the ITR. The actions have multiple forms in order to adapt to a variety of teaching-learning situations. Specifically, presentation of examples reduces the cognitive load for students in complex problem-solving tasks, and timely feedback provides information for correcting students' errors and misconceptions, meanwhile it enhances motivations for higher levels of effort [23]. The specific design of action sequence relies heavily on selecting tutoring strategies. The strategy may be inferred from either mimicking human teaching such as apprenticeship [49], or a variety of learning theories such as cognitive learning [50], constructivism [51], and situated learning [52].