# Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines
## 5 Discussion and Conclusions  
This paper transforms the relationship model describing the teaching-learning process into the well-established perception-planning-action ITR model in the area of AI.  
* The first phase of initiating an effective perception-planning-action loop of ITR is to acquire sufficient information concerning the environments and the students, so it introduces multi-modal perception that utilizes multiple communication channels. Then, the ITR leads the students to do a variety of activities for external-assessment and self-assessment, so it can gather information, and then implement statistical machine learning methods to analyze the students' learning style and knowledge level.
* According to students' learning style and knowledge level, the second phase of ITR builds student models for each student or a group of students, based on which it can predict short-term and long-term outcomes of delivering certain teaching contents via specific teaching strategies. Then, the ITR may interact with the student models with multiple candidate plans of teaching contents and strategies and make predictions on the outcome of each candidate plan. Human tutors and curriculum designers may also intervene in the decision-making process.
* After receiving the plan result, the third phase is activated by the action module of the ITR that resolves the action sequence indicating what, when, and how teaching contents are to be delivered to the students. The action module needs to form an appropriate scenario for the students, as well as to provide effective communication channels for transmitting the teaching contents and receiving the feedback from students, using scene construction and multi-modal communication channels in the action module.
* With the rapid progress of AI techniques, many open research areas may be defined for ITRs with the aid of the perception-planning-action model.
* Perception: Multi-modal data fusion is not fully researched in the context of ITR design, even though the topic is well-investigated and still in heated discussion in the field of data-mining and robotics. Additionally, little work has compared the effects of different factors that indicate learning styles. Hence, it remains an open question on how to select the appropriate set of variables to predict a students' learning process.
* Planning: In the context of student modelling, the model tracing, constrained model, and machine learning methods have their own application scenarios and limitations. Therefore, the recent advances of explainable AI may be incorporated in ITRs. In terms of the decision-making process, limited research has contributed to the problem formulation that takes into account the multi-objective problems such as jointly considering the long-term education objective, short-term curriculum objective, and student personal objective.
* Action: The action module field turns out to be the most researched area with state-of-art AI techniques. Even so, the applications of advanced AI techniques in ITRs such as generated adversarial networks are underway, while the design of physically embodied robots is far from mature.  
**Author Contributions:** All authors contributed equally to this work.  
**Funding:** The National Natural Science Foundation of China, grant number 61601486 and 91648204, funded this research.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.