# Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines
## 3 Artificial Intelligence Techniques for Designing Intelligent Tutor Robots
### Multi-Modal Perception of Students
#### 3.1.2 Learning Style and Knowledge Mastery Analysis  
With the aid of multi-modal data fusion, the ITR may gather data about the students with external-assessment and self-assessment, and then implement statistical machine learning methods to analyze the students' learning style and knowledge level.  
Before delving into the external and self-assessment methods, the ITR selects the criteria for a learning style. Although a wide range of learning style theories have been developed in the literature, most of them cannot be compared quantitatively so that it is hard to argue only one of them could outperform others [4]. Therefore, the human-tutor may have to analyze the curriculum and select the learning style theory for the ITR and, most importantly, the learning style theory should be projected to a well-defined model in terms of categorization and quantification. For example, the [PERSON]'s model [35] proposes the well-known four-dimension model for learning styles: perception (Sensory/Intuitive), information input (Image or Verbal), information process (Active or Reflective), and understanding (Sequential or Global). Each dimension may be labelled in supervised learning and be quantitatively scaled.  
After selecting the learning style model, an external-assessment may be implemented during the teaching process, which imitates human-tutors' capabilities of observing students' activities and affections to assess the learning status. By analyzing the heterogeneous data gathered from multi-modal sensors, machine learning techniques may extract hidden modes of the students' learning style and status affected by personality, motivation, and emotion [30]. The students' self-assessment that incorporates levels of competencies for each concept or skill may also be implemented, which allows the ITR to continuously calibrate planning and actions to better meet student needs. In this case, a simple way for self-assessment questionnaires is to use a scale that ranges from a cursory level and a factual knowledge level to a conceptual knowledge level and application level. With the aid of simple statistical machine learning techniques and visualized analytics like bar charts, radar charts, and ranking tables, information about the per-student knowledge mastery level may be extracted [36; 37].  
More sophisticated concept maps may be adopted to analyze students' level and organization of knowledge. The knowledge along with its structure for mastering a curriculum heavily relies on the evaluation and experience of human-tutors. Hence, the ITR may receive inputs from human-tutors, and a well-designed and formalized data structure for the human-robot tutor interface is the concept map and knowledge graph [38; 39]. In a concept map or knowledge graph, each vertex is a concept or ontology and a network is formed to describe the relationship between ontologies. Based on a series of related knowledge concepts and a standard concept map designed by human experts, the ITR may request students to construct a knowledge graph [23; 39] representing everything that they know about the topic, and then use machine learning techniques designed for graph networks [40; 41] to evaluate the density and intensity of the students' knowledge structures. This reveals students' blind spots and weak knowledge connections that need to be strengthened during the follow-up teaching and learning process. Take an example shown in Figure 3. The knowledge graph may be compared with a target graph for the curriculum by the knowledge level analysis module, so that the disparity graph may be generated to reveal the weak and missing knowledge of a student.  
The extracted information indicating students' learning style and knowledge levels may be jointly fed to the planning modules of the ITR.