Introduction In today’s modern world, computers play an important part in human lives. Developers from all over the world have invented different applications, which help users to perform tasks with ease. The applications range from those in the social field to those designed for educational purposes. Clark, Fox, and Lappin (2010, p. 65) observe that learning software has a number of functionalities. The uses include diagnosing and handling of errors, lesson tutoring, archival of documents archival, and language test. One of the software developed to help students learn is Intelligent Language Tutoring Systems (ILTSs). According to Kumar (2011, p. 2), ILTSs are computer systems designed to provide learners with feedback and instructions in the absence of a teacher. According to Durlach and Lesgold (2012, p. 55), there are numerous types of ILTSs. Each of them has its pros and cons. The applications are used in different settings, such as in professional and formal education. In spite of the fact that there are a number of ILTSs, computer systems are designed to meet a common goal among learners. The primary purpose of the software is to reduce scholars’ over-dependency on tutors. Graf (2012, p. 78) observes that the applications deliver erudition materials to every student through the cyberspace channel. To enable free input of sentences and phrases, Intelligent Language Tutoring Systems apply Natural Language Processing (NLP) practices. NLP is a component of artificial intelligence, computer science, and computational linguistics (Kumar 2011). It focuses on the relations between human and computer languages. Consequently, NLP is defined as the ability of computer applications to comprehend spoken speech in natural languages, such as English, and not in artificial forms, such as C++. In this paper, the author will discuss in detail the relevant aspects of Intelligent Language Tutoring Systems and NLP. The writer will shed more light on different kinds of ILTSs, such as web-based German Tutor, auto-tutor, and CIRCSIM-Tutor. In addition, the author will discuss the aspects and characteristics of the Arabic Language in relation to NLP. The rest of this paper is organized as follows: section two provides background on Natural Language Processing, Intelligent Language Tutoring Systems, and characteristics and aspects of Arabic Lingo. Section three of the paper will discuss the development and progression of Arab ILTS in comparison to other languages. Section four will focus on the future prospects of the study. Finally, section five will concentrate on the discussion and conclusion of the literature. Background Intelligent Tutoring Systems The possibility of intelligence contraptions has been a subject of discussion for centuries. The notion of using intelligent machines for instructional application is traced back to 1924 (Fulcher & Jain 2008). It is the year when Sidney Pressey from Ohio State University developed a contrivance to instruct scholars in the absence of a tutor. The device, which resembled a typewriter, was fitted with a number of keys and a window for displaying questions. Nkambou, Bourdeau, and Mizoguchi (2010, p. 98) hold that the learning machine offered a response to students by recording