OVERVIEW OF INTELLIGENT TUTORING SYSTEMS
Intelligent Tutoring Systems research—more broadly known as the field of “AI and Education” [1]—has been concerned with individualized instruction in many different domains, with a variety of representational media and interactive methods [27]. It should be mentioned at the outset that ITS systems work, and they can work quite well at teaching STEM topics compared to teacher-to-student tutoring [1]. Furthermore, it is important to note that ITS systems generally have not been intended as replacements for human teachers or tutors, but rather designed as tools to assist in classwork and for independent learning.
ITS work can be traced to the 1960s with the development of AI programs that represent knowledge in structured models , especially semantic nets, production rules, and schemas/frames. Programs using such models can solve problems, such as answering factual questions, proving theorems, and manipulating mathematical equations. Subsequent research in the 1970s added a reasoning module that interprets the structured model in particular situations for professional tasks, such as diagnosis, planning, design, and process control; these programs were called “expert systems.”
In general, an intelligent tutoring program contains such an AI problem-solving program, using it to interact with and instruct a student. Thus ITS is contrasted with computer-based instruction programs that do not have a built-in capability to solve the problems that are presented to students.
Most intelligent tutoring programs engage the student in a learning activity in which the program serves as an instructor; they use distinct models of the domain, the student, and curriculum; and the interactive design is based on a theory of the pedagogical process [13, 23, 24, 27].
ITS research has been concerned with teaching mathematics [1] and basic science, as well as professional expertise relating to complicated systems, such as electro-mechanical troubleshooting [3], engineering operations [16], and medicine [9]. Insofar as machine learning programs are complicated systems whose capabilities and, to some extent, methods we want users to understand, the techniques and lessons from ITS research and development over nearly 50 years are worth considering for adoption in XAI research.