A Survey of Intelligent Language Tutoring Systems

Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life FLT, in this article we investigate the connection between FLT activity design and the system architecture of an ILT system. We argue that a demand-driven, annotation-based natural language processing (NLP) architecture is well-suited to handle the demands posed by the heterogeneous learner input which results when supporting a wider range of FLT activity types. We illustrate how the unstructured information management architecture (UIMA) can be used in an ILTS, thereby connecting the specific needs of activities in foreign language teaching to the current research and development of NLP architectures in general. Making the conceptual issues concrete, we discuss the design and realization of a UIMA-based reimplementation of the NLP in the TAGARELA system, an intelligent web-based tutoring system supporting the teaching and learning of Portuguese.

By embedding the function of automatically correcting nearly free format English translations of given Japanese sentences, we have developed a new ILTS(intelligent language tutoring system) for improving English Writing Skills in a L2 tutoring environment. In addition to a diagnostic engine capable of identifying grammatical, lexical and word usage errors of students' input translations and returning error contingent feedback, we have developed a simple table look-up parser for displying the grammar trees. The table look-up parser parses a user input, which is always erronous, by simply matching the extended part-of-speech tag sequence of a closing sentence in a template of ILTS to the entries of a look-up-table, in which each extended part-of-speech tag array corresponds to one grammar tree. The complexity of the table look-up parser is O(n), where n denotes the length of a sentence. This shows a marked improvement over the general purpose parser of which the complexity is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ).

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

Starting with early 1960's a new approach has been followed by researchers and specialists in developing instructional applications. This new approach was related to the use, as an experiment, of the Natural Language Processing (NLP) in the development of next generation of instructional software. Soon, the experiment has became a reality and a necessity in the context that the quantity of information which had to be processed by students, regardless the domain or discipline, has increased considerably. One of the main goals chased constantly by the researchers and specialists in NLP was related to the improvement of the systems, methods, and techniques used for students' knowledge assessment using NLP facilities. A great opportunity to reach valuable results has arisen once the Intelligent Tutoring Systems (ITS) have been considered into the equation. Taking into consideration the ITS declared purposes to address instructional specific activities such as, teaching, learning, performance assessment and feedback generation/communication/analysis, a merge of the ITS and NLP took place and this merge was in the evident benefit of both, teachers and students. Of course, in all these changes a central role has been played by the progress related to the Information and Communication Technologies (ITC) and mainly by the development of few critical hardware and software components, such as, computers, mobile devices, operating systems for these devices and programming languages. This paper is presenting the author proposal of an automatic system for generating questions and performing the analysis of the students' answers, the system being encapsulated in an ITS using NLP and which is applicable to the Computers Programming and C language discipline. Also, the author will present briefly the algorithm used for the development of the proposed system, with the primary target to detect the incorrect answers based on off-topic/on-topic method.

The rapid advancement of artificial intelligence (AI) technologies has significantly reshaped educational systems and learning environments worldwide. In particular, the integration of AI into language education has created new opportunities for improving learning efficiency, enhancing personalized instruction, and facilitating interactive communication environments. In the context of globalization and international talent development, the ability to communicate effectively in English has become an essential competence for participating in global economic, cultural, and technological exchanges. However, traditional language education models often rely on teacher-centered instruction and standardized assessments, which may not fully address the diverse learning needs of students or provide sufficient opportunities for authentic communication practice. This study explores the role of artificial intelligence in empowering the development of English competence for international talents. Drawing upon theories of technology-enhanced learning, intelligent tutoring systems, and global competence development, the paper proposes a conceptual framework that illustrates how AI technologies can support language learning through multiple application scenarios. These scenarios include intelligent language tutoring systems, AI-mediated communication environments, automated language assessment systems, and data-driven learning analytics. Based on these application scenarios, the study further proposes an AI-empowered educational model that integrates artificial intelligence technologies with innovative pedagogical approaches to support the cultivation of international talents. The model emphasizes personalized learning pathways, adaptive instruction, and global communication platforms supported by AI systems. The study contributes to theoretical discussions on AI-supported language education and provides practical insights for cultivating internationally competitive talents in the era of digital transformation.

The inspection of documents written in natural language with computers has become feasible thanks to the advances in Natural Language Processing (NLP) techniques. However, certain applications require a deeper semantic analysis of the text to produce good results. In this article, we present an exploratory study of semantic-aware NLP techniques for discovering latent concerns in use case specifications. For this purpose, we propose two NLP techniques, namely: semantic clustering and semantically-enriched rules. After evaluating these two techniques and comparing them with a technique developed by other researchers, results have showed that semantic NLP techniques hold great potential for detecting candidate concerns. Particularly, if these techniques are properly configured, they can help to reduce the efforts of requirement analysts and promote better quality in software development.

This pioneering article aims to understand hybrid tutoring strategies, which combine human and artificial tutor interventions. In this regard, an approach of an Intelligent Tutoring System (ITS) was developed, involving the intervention of human tutors to mediate student engagement in e-learning. To evaluate the effectiveness of the approach, testing guidelines were adopted involving human tutors from the stages of system preparation, activity script creation and interview, test application, data collection, analysis, and communication of iteration results and proposals for improving the ITS approach. The results suggest effectiveness in increasing productivity and facilitating tutoring activities. Additionally, there was a lower perception of complexity, burden, and consistency when continuously iterating the system with the involvement of human tutors. Therefore, strategies such as “active search” for students with low engagement levels were facilitated by algorithms from the ITS approach, with the contribution and involvement of interpersonal interactions of human tutors to provide students with attractive and personalized tutoring experiences.

This research assessed how the performance and team skills of three-person teams working with an Intelligent Team Tutoring System (ITTS) on a virtual military surveillance task were affected by feedback privacy, participant role, task experience, prior team experience, and teammate familiarity. Previous work in Intelligent Tutoring Systems (ITSs) has focused on outcomes for task skill training for individual learners. As research extends into intelligent tutoring for teams, both task skills and team skills are necessary for good team performance. This work includes a brief review of previous research on ITTSs, feedback, teams, and teamwork, including the recounting of two categories of a framework of teamwork performance, Communication and Cognition, which are relevant to the present study. This research examines the effects of an intelligent agent, as well as features of the team, its members, and the task being undertaken, on team communication (measured by relevant key-presses) and team situation awareness (as measured by scores on a quiz). Thirty-seven teams of three participants, each at their own computer running a multiplayer surveillance simulation, were given just-in-time private (individually delivered) or public (team-delivered) performance feedback during four 5-min trials. In the fourth trial, two of the three participants switched roles. Feedback type, teamwork experience, and teammate familiarity had no statistically significant effect on communication or team situation awareness. However, higher levels of role experience and task experience showed significant and medium-sized effects on communication performance. Results, based on performance data and structured interview responses, also revealed areas of improvement in future feedback design and a potential benchmark for feedback frequency in an action-oriented serious game-based ITTS. Among the conclusions are six design objectives for future ITTSs, establishing a foundation for future research on designing effective ITTSs that train interpersonal skills to nascent teams.

This article is a review of experiments comparing the effectiveness of human tutoring, computer tutoring, and no tutoring. “No tutoring” refers to instruction that teaches the same content without tutoring. The computer tutoring systems were divided by their granularity of the user interface interaction into answer-based, step-based, and substep-based tutoring systems. Most intelligent tutoring systems have step-based or substep-based granularities of interaction, whereas most other tutoring systems (often called CAI, CBT, or CAL systems) have answer-based user interfaces. It is widely believed as the granularity of tutoring decreases, the effectiveness increases. In particular, when compared to No tutoring, the effect sizes of answer-based tutoring systems, intelligent tutoring systems, and adult human tutors are believed to be d = 0.3, 1.0, and 2.0 respectively. This review did not confirm these beliefs. Instead, it found that the effect size of human tutoring was much lower: d = 0.79. Moreover, the effect size of intelligent tutoring systems was 0.76, so they are nearly as effective as human tutoring.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

Today is the age of Advancement in the various types of machines or devices. And due to this advance. Attribute the difference between human and machine decrease. And the technology, of the computer-based learning system, are integrating more towards Advance tools and technologies like (A.I, N.L.P.)”, such a system is known as an intelligent-tutoring system or conversation system, like a chatbot. It is necessary that with rapidly changing technology, teaching and learning methods of the education system should adapt to increase and enhance the capabilities of teachers and abilities of the students. Therefore, the intelligent tutoring system (Chatbot) using natural language which allows humans to interact with a computer is one of the best alternatives of these requirements where the human tutor is replacing with a virtual tutor. Nowadays, an intelligent tutoring system(Chatbots) are in high demands in our various fields like teaching and learning in education, business, e-comm, online trading, public health care, and sports etc. Different techniques and processes are involved in designing and developing chatbots. This review paper aims to take a survey of the critical and necessary methods used in this communication and conversational agents.

This paper discusses the inherent goals and trade-offs involved in the design of an efficient, robust Web tutor within the context of a working, hypermedia framework. The focus is on the German Tutor, an Intelligent Language Tutoring System (ILTS) that implements generality, interactivity, and modularity as contrasted with more traditional computational constraints and architectures. Design and pedagogical goals of Web-based delivery, such as intelligence and efficiency, will also be addressed but again with an emphasis on the special requirements of efficient, adaptive hypermedia systems.

Intelligent tutoring systems (ITSs) are computer programs that provide instruction adapted to the needs of individual students. Dialog systems are computer programs that communicate with human users by using natural language. This paper presents a systematic literature review to address ITSs that incorporate dialog systems and have been implemented in the last twenty years. The review found 33 ITSs and focused on answering the following five research questions. a) What ITSs with natural language dialogue have been developed? b) What is the main purpose of the tutoring dialogue in each system? c) What are the pedagogical features of the teaching process performed by the ITSs with natural language dialogue? d) What natural language understanding approach does each system employ to understand students’ utterances? e) What evidence exists related to the evaluation of ITSs with natural language dialogue? The results of this review reveal that most ITSs are directed toward science, technology, engineering, and mathematics (STEM) domains at the university level, and the majority of the selected ITSs implement the expectations and misconceptions tailored approach. Furthermore, most ITSs use dialog to help students learn how to solve a problem by applying rules, laws, etc. (the apply level in Bloom’s taxonomy). With regard to the instructional approach, the selected ITSs help students write correct explanations or answers for deep questions; assist students in problem solving; or support a reflective dialogue motivated by either previously provided content or the result of a simulation. Additionally, we found empirical evaluations for 90.91% of the selected ITSs that measure the learning gains and/or assess the impacts of different tutoring strategies.

Cyberlearning is challenged by the lack of personal and assessment-driven learning, and students are often puzzled by the lack of instructor guidance and feedback, the huge volume and diversity of the learning materials, and thelack of the ability to zoom in from the general concepts to the more specific ones, or the opposite. Intelligent tutoring systems are needed to improve the cyberlearning quality. One of the major difficulties is knowledge representation. The current industry standard is to use Web Ontology Language (OWL) forrepresenting knowledge structure. But OWL only supports one first-class relation, is-a, between the concepts, and different knowledge areas usually need different custom relations to describe the relations among the concepts. For example part-of and time dependency are important relations torepresent most engineering knowledge bodies. OWL has to use object properties to emulate such custom relations, leading to awkward knowledge representation hard for domain experts to code, validate and use such knowledge bases. This research uses Pace University's extension to OWL, named Knowledge Graph (KG), to support knowledge representation with custom relations. The instructors can use Pace University extended Protege IDE to declare and apply custom relations in a single document. The instructor teaching experience is also coded in the KG to better support custom learning order by students with different backgrounds. The prototype of a knowledge-driven tutoring system was designed and implemented to illustrate how the KG supports integrated assessments, using assessment results to custom student learning order or material, and let the students freely navigate in the knowledge space from general to specific or the opposite, and following various custom relations. A web technology tutorial is used to validate the design and effectiveness of this approach.

Due to the rapid growth of the use of computers and increasing use of the Internet in education a large number of Web-based educational applications have been developed and implemented. However, very few of them are pedagogically intelligent and interactive for learning purposes. The Web-based intelligent learning has become more effective in the past decade due to increasing use of the Internet in education. A literature search indicates that there is a lack of relevant comprehensive research concerning the efficiency of computer-assisted instructions used in engineering education. The main focus of the research described in this chapter is on the comprehensive review of design and development of the Web-based authoring tool for an Intelligent Tutoring System in engineering education. The chapter outlines and discusses important issues of the development of Intelligent Tutoring System (ITS) in engineering education with an example of the development of a Web-Based Computer-Assisted Tutorials and Laboratory Procedures (WCALP).KeywordsEducational SoftwareExpert ModelInstructional SoftwareEducational Technology PublicationNatural Language DialogueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.