Linguistic Constructs Represent the Domain Model in Intelligent Language Tutoring (2023)

Abstract: This paper presents the development of the AI-based language-learning platform, Revita. It is an intelligent online tutor, developed to support learners of multiple languages, from lower-intermediate toward advanced levels. It has been in pilot use with hundreds of students at several universities, whose feedback and needs shape the development. One of the main emerging features of Revita is the system of linguistic constructs to represent the domain knowledge. The system of constructs is developed in collaboration with experts in language pedagogy. Constructs define the types of exercises, the content of the feedback, and enable detailed modeling and evaluation of learner progress.

## introduction the focus of this paper is the novel domain model in revita, 1 an intelligent tutoring system (its) for language learning @xcite . revita follows the classic design of its-with a domain model, student model, and instruction model. the domain model describes what must be mastered by the learner: concepts, rules, etc.-known as skills in its literature-and relationships among them @xcite . we represent the domain model as a system of linguistic constructs-a wide range of linguistic phenomena, including inflexion of various paradigms, government relations, collocations, complex syntactic constructions, etc. the system of constructs is developed in collaboration with experts in language teaching. it impacts all apcects and components of revita-the variety of exercises that it generates automatically, the intelligent feedback given to the learner, modeling of learner knowledge, and evaluation of learner progress. the student model represents the learner's proficiency. it is based on the history of answers given by the learner to many exercises, and tries to build a detailed picture of what the user knows vs. does not know. the instruction model embodies the pedagogical principles that lie behind the decisions: which exercises the learner is best prepared for next, and which feedback should be provided to guide the learner toward the right answer. these models are interconnected in its. revita is currently undergoing pilot studies with real-world learners and teachers at several universities @xcite . revita's main target group are learners who have passed beyond the beginner level -above a2 on the cefr scale. 2 revita is developed as a tool for learners and teachers of several languages: finnish and russian are currently the most developed languages. several "beta" languages, including italian, german, swedish, and others, are in earlier stages of development. the user interface also works in several languages (english, finnish, russian, chinese, italian). revita is not meant to replace the teacher. for students, it provides 24/7 access to an unlimited amount of personalized exercises for practice matching the learner's current level, with immediate feedback and progress estimation. for teachers, it provides time-saving benefits by allowing them to delegate the mundane work of creating hundreds of exercises for each topic for students at different levels. revita allows the teachers to share learning materials, create their own exercises, work with groups, and monitor progress and evaluation. the paper is organized as follows: section 2 briefly reviews work on intelligent computerassisted language learning (icall). the principles and ideas behind revita are described in section 3. it also describes its main components: linguistic constructs, automatic generation of exercises and feedback, and modeling of learner knowledge. section 4 describes tools for learners and teachers. section presents the conclusions and future work. ## prior work interest in computer-assisted language learning (call) is growing with the rapid development of language technology. call is seen as the "study of applications of the computer in language teaching and learning" @xcite . applying its to language learning and supporting call by intelligent and/or adaptive methodologies, such as expert systems (es), natural language processing (nlp), automatic speech recognition (asr), etc.is the domain of intelligent call, or icall. the goal of icall is building advanced applications for language learning using nlp and linguistic resources-corpora, lexicons, etc. @xcite . the number of academic and commercial tools for language learning is growing drastically, e.g., popular commercial systems like duolingo, rosetta stone, babbel, busuu, italki, etc. some call systems aim to give learners access to authentic materials @xcite , the opportunity to interact with teachers and native speakers (e.g., the app lingoda is a platform for live video classes), and provide text or sound feedback based on learner needs and knowledge @xcite . modern call systems are also mobile, which increases their accessibility @xcite @xcite . in developing call, pedagogical goals-rather than technological means-should be the primary focus @xcite . it has been shown that using icall systems for education improves learner motivation and attitudes, increases options for self-study @xcite , improves retention of various learning concepts, communication between students and teachers, academic selfefficacy @xcite , and overall language skills @xcite . despite having existed for decades, icall still has a number of serious limitations to overcome. apart from platforms where teachers directly interact with students in video classes, most existing icall systems are based on the so-called "canned" approach-the available sets of exercises are premade, and therefore limited. this limits the level of personalization: pre-made exercises can be arranged into different individual programs, but no personalized exercises can be provided. 3 core components of revita ## tools for students and teachers at any time, the student can set her cefr proficiency level manually or take an adaptive placement test to estimate proficiency (see the button "adaptive test" on figure 1 ). the test draws on a bank of questions prepared by teachers; the sampling of questions is driven by an irt model trained on learner data. after that, the estimate of the learner's proficiency levels is adjusted according to the correctness of answers to exercises. the learner can upload a story from any website or a local file. to each uploaded text, revita applies classification by semantic topic-culture, science, sport, politics-and difficulty classifiers. in case the learner does not want to choose a text for practice, there is a "dive-in" option to practice with a randomly sampled story from a selected category (private vs. public or story tagged by a semantic topic). another option is to choose a "story of the day" suggested by revita. the preview mode (see figure 2 ) allows the user to read a story, edit it (in case it was extracted from a web page inaccurately), and review the grammatical topics that can be learnt through practice with this story. clicking on each word provides its translation into a number of languages. the learner can mark whether she knows a word or not. all unfamiliar words are added to the learner's personal set of flashcards, which are used for vocabulary practice. the practice mode presents the grammar and listening exercises-the learner can hear a segment of text in context and is expected to type the missing words correctly in the empty box. the user can also practice with a story in the competition mode, against a bot: the difference from normal practice is that the learner needs to do the exercises faster than the bot-whose skill levels approximately match those of the learner. another option is to practice with a crossword built on the authentic text-the translations of words are used as hints. practice mode also allows the user to create notes during practice, which can be attached to words in the story. all learner's notes are collected together (see button "notes" in figure 1 ) for easy review; each note has a reference to words in the story it was attached to. revita offers various statistics and info-graphics to track progress on grammar constructs and vocabulary. these analytics are available to the learner and to the teacher. revita allows teachers to build groups of students, share texts with them, and create tailored exercises that can be shared with the group. revita allows teachers to track how their students practice and how well they perform on various tasks. ## conclusions and future work this paper presents an in-depth discussion of the novel core component of the revita language learning system-the domain model embodied in a system of linguistic constructs. this system of constructs underlies and supports all aspects of the learning experience in revita, it supports the generation of the quality exercises and feedback. it also supports the modeling of learner skills more accurately to provide informative progress analytics, and to offer exercises most appropriate for the learner's current level. we have results from pilot studies with finnish and russian l2 learners using the new domain model, but the discussion of the results is beyond the scope of this paper. in the future, we plan to improve the domain model by adding more information about the interactions and dependencies among the constructs-which will enable the creation of more intelligent learning paths. we also plan to add new types of activities, e.g., speech exercises.