Intelligent tutoring system
An intelligent tutoring system is a computer system that imitates human tutors and aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher, or no teacher at all. ITSs are often designed with the goal of providing access to high quality education to each and every student.
History
Early mechanical systems
The possibility of intelligent machines has been discussed for centuries. Blaise Pascal created the first calculating machine capable of mathematical functions in the 17th century simply called Pascal's Calculator. At this time the mathematician and philosopher Gottfried Wilhelm Leibniz envisioned machines capable of reasoning and applying rules of logic to settle disputes. These early works inspired later developments.The concept of intelligent machines for instructional use date back as early as 1924, when Sidney Pressey of Ohio State University created a mechanical teaching machine to instruct students without a human teacher. His machine resembled closely a typewriter with several keys and a window that provided the learner with questions. The Pressey Machine allowed user input and provided immediate feedback by recording their score on a counter.
Pressey was influenced by Edward L. Thorndike, a learning theorist and educational psychologist at the Columbia University Teachers' College of the late 19th and early 20th centuries. Thorndike posited laws for maximizing learning. Thorndike's laws included the law of effect, the law of exercise, and the law of recency. By later standards, Pressey's teaching and testing machine would not be considered intelligent as it was mechanically run and was based on one question and answer at a time, but it set an early precedent for future projects.
By the 1950s and 1960s, new perspectives on learning were emerging. Burrhus Frederic "B.F." Skinner at Harvard University did not agree with Thorndike's learning theory of connectionism or Pressey's teaching machine. Rather, Skinner was a behaviorist who believed that learners should construct their answers and not rely on recognition. He too, constructed a teaching machine with an incremental mechanical system that would reward students for correct responses to questions.
Early electronic systems
In the period following the second world war, mechanical binary systems gave way to binary based electronic machines. These machines were considered intelligent when compared to their mechanical counterparts as they had the capacity to make logical decisions. However, the study of defining and recognizing a machine intelligence was still in its infancy.Alan Turing, a mathematician, logician and computer scientist, linked computing systems to thinking. One of his most notable papers outlined a hypothetical test to assess the intelligence of a machine which came to be known as the Turing test. Essentially, the test would have a person communicate with two other agents, a human and a computer asking questions to both recipients. The computer passes the test if it can respond in such a way that the human posing the questions cannot differentiate between the other human and the computer. The Turing test has been used in its essence for more than two decades as a model for current ITS development. The main ideal for ITS systems is to effectively communicate. As early as the 1950s programs were emerging displaying intelligent features. Turing's work as well as later projects by researchers such as Allen Newell, Clifford Shaw, and Herb Simon showed programs capable of creating logical proofs and theorems. Their program, The Logic Theorist exhibited complex symbol manipulation and even generation of new information without direct human control and is considered by some to be the first AI program. Such breakthroughs would inspire the new field of Artificial Intelligence officially named in 1956 by John McCarthy at the Dartmouth Conference. This conference was the first of its kind that was devoted to scientists and research in the field of AI.
Image:Platovterm1981.jpg|right|thumb|The PLATO V CAI terminal in 1981
The latter part of the 1960s and 1970s saw many new CAI projects that built on advances in computer science. The creation of the ALGOL programming language in 1958 enabled many schools and universities to begin developing Computer Assisted Instruction programs. Major computer vendors and federal agencies in the US such as IBM, HP, and the National Science Foundation funded the development of these projects. Early implementations in education focused on programmed instruction, a structure based on a computerized input-output system. Although many supported this form of instruction, there was limited evidence supporting its effectiveness. The programming language LOGO was created in 1967 by Wally Feurzeig, Cynthia Solomon, and Seymour Papert as a language streamlined for education. PLATO, an educational terminal featuring displays, animations, and touch controls that could store and deliver large amounts of course material, was developed by Donald Bitzer in the University of Illinois in the early 1970s. Along with these, many other CAI projects were initiated in many countries including the US, the UK, and Canada.
At the same time that CAI was gaining interest, Jaime Carbonell suggested that computers could act as a teacher rather than just a tool. A new perspective would emerge that focused on the use of computers to intelligently coach students called Intelligent Computer Assisted Instruction or Intelligent Tutoring Systems. Where CAI used a behaviourist perspective on learning based on Skinner's theories, ITS drew from work in cognitive psychology, computer science, and especially artificial intelligence. There was a shift in AI research at this time as systems moved from the logic focus of the previous decade to knowledge based systems—systems could make intelligent decisions based on prior knowledge. Such a program was created by Seymour Papert and Ira Goldstein who created Dendral, a system that predicted possible chemical structures from existing data. Further work began to showcase analogical reasoning and language processing. These changes with a focus on knowledge had big implications for how computers could be used in instruction. The technical requirements of ITS, however, proved to be higher and more complex than CAI systems and ITS systems would find limited success at this time.
Towards the latter part of the 1970s interest in CAI technologies began to wane. Computers were still expensive and not as available as expected. Developers and instructors were reacting negatively to the high cost of developing CAI programs, the inadequate provision for instructor training, and the lack of resources.
Microcomputers and intelligent systems
The microcomputer revolution in the late 1970s and early 1980s helped to revive CAI development and jumpstart development of ITS systems. Personal computers such as the Apple II, Commodore PET, and TRS-80 reduced the resources required to own computers and by 1981, 50% of US schools were using computers. Several CAI projects utilized the Apple 2 as a system to deliver CAI programs in high schools and universities including the British Columbia Project and California State University Project in 1981.The early 1980s would also see Intelligent Computer-Assisted Instruction and ITS goals diverge from their roots in CAI. As CAI became increasingly focused on deeper interactions with content created for a specific area of interest, ITS sought to create systems that focused on knowledge of the task and the ability to generalize that knowledge in non-specific ways. The key goals set out for ITS were to be able to teach a task as well as perform it, adapting dynamically to its situation. In the transition from CAI to ICAI systems, the computer would have to distinguish not only between the correct and incorrect response but the type of incorrect response to adjust the type of instruction. Research in Artificial Intelligence and Cognitive Psychology fueled the new principles of ITS. Psychologists considered how a computer could solve problems and perform 'intelligent' activities. An ITS programme would have to be able to represent, store and retrieve knowledge and even search its own database to derive its own new knowledge to respond to learner's questions. Basically, early specifications for ITS or require it to "diagnose errors and tailor remediation based on the diagnosis". The idea of diagnosis and remediation is still in use today when programming ITS.
A key breakthrough in ITS research was the creation of The LISP Tutor, a program that implemented ITS principles in a practical way and showed promising effects increasing student performance. The LISP Tutor was developed and researched in 1983 as an ITS system for teaching students the LISP programming language. The LISP Tutor could identify mistakes and provide constructive feedback to students while they were performing the exercise. The system was found to decrease the time required to complete the exercises while improving student test scores. Other ITS systems beginning to develop around this time include TUTOR created by Logica in 1984 as a general instructional tool and PARNASSUS created in Carnegie Mellon University in 1989 for language instruction.