Intelligent tutoring system

An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without 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 aims to solve the problem of over-dependency of students over teachers for quality education. It aims to provide access to high quality education to each and every student, thus reforming the entire education system.

The possibility of intelligent machines have 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 (Buchanan, 2006). These early works contributed to the development of the computer and future applications.

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 himself was influenced by Edward L. Thorndike, a learning theorist and educational psychologist at the Columbia University Teacher 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. Following later standards, Pressey's teaching and testing machine would not 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 behaviourist who believed that learners should construct their answers and not rely on recognition. He too, constructed a teaching machine structured using an incremental mechanical system that would reward students for correct responses to questions.

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