Intelligent Tutor to Support Teaching and Learning of T atar (2024)

Abstract: This paper presents our work on tools to support the Tatar language, using Revita, a web-based Intelligent Tutoring System for language teaching and learning. The system allows the users — teachers and learners — to upload arbitrary authentic texts, and automatically creates exercises based on these texts that engage the learners in active production of language. It provides graduated feedback when they make mistakes, and performs continuous assessment, based on which the system selects exercises for the learners at the appropriate level. The assessment also helps the students maintain their learning pace, and helps the teachers to monitor their progress.The paper describes the functionality currently implemented for Tatar, which enables learners — who possess basic proficiency beyond the beginner level — to improve their competency, using texts of their choice as learning content. Support for Tatar is being developed to increase public interest in learning the language of this important regional minority, as well as to to provide tools for improving fluency to “heritage speakers” — those who have substantial passive competency, but lack active fluency and need support for regular practice.

## introduction tatar is a minority language spoken in the russian federation and by the tatar diaspora worldwide. although tatar is an important turkic language with over seven million speakers, it remains a lowresource language from the technological perspective, with little language technology to support its wider use online. this reduced online presence, in turn, limits and diminishes the overall vitality of the language. interest in second-language (l2) learning is continually increasing, with a growing number of resources available for learners at various proficiency levels. however, most of these resources either provide only an elementary introduction to the basics of the language, or try to increase proficiency by memorizing advanced vocabulary or complex grammatical structures, such as verb tenses. despite this variety, it is difficult to find tools that make the learning process interactive and personalized-engaging the learners' interests and adapting to their level. the revita approach to language learning and teaching foot_0 is founded on allowing the users themselvesstudents or teachers-to select any authentic material as learning content. the system then automatically generates exercises based on the chosen content, monitors the learner's performance on these exercises to assess the learner's proficiency in multiple dimensions, and adjusts the difficulty of the exercises according to the learner's current level. currently, there is no similar online service for teaching tatar to non-beginner students, using text material chosen by the students themselves. implementing this plan will enable anyone to learn tatar, using the latest methods from artificial intelligence and language technology. creating opportunities for learning tatar and promoting its use within speaker communities is of great importance to supporting the language. tatars form the largest linguistic minority in russia, with diasporas in many other countries. it is crucial to stimulate interest in learning this language to preserve its heritage and expand its use geographically. the need to create and maintain learning platforms such as revita, as well as the importance of supporting the study of the tatar language, underlies the relevance of this work. intelligent support for language learning is a rapidly evolving and complex area of research. the problem becomes especially challenging in the case of low-resource languages: on one hand, the need is more urgent, since many of the low-resource languages are endangered, and their speaker communities urgently require support. on the other hand, building intelligent tools for such languages is much more difficult due to the paucity of foundational tools and resources. some work is being done in this direction, e.g., by apertium @xcite @xcite . however, the availability of natural language processing (nlp) resources for turkic languages, particularly the endangered ones, still lags far behind that of, e.g., the major european languages. this paper introduces and describes the work on tatar in the revita system. section 2 outlines the broad principles and capabilities of revita, describes the work on the system, and explains the notion of "construct" within the framework. section 3 details all the constructs implemented in the revita platform and provides examples of exercises that can be created based on these constructs. section 4 summarizes the results achieved during the adaptation of revita to the tatar language, and outlines the next steps for future work. ## conclusions this paper presents the current state of our work on adapting, revita, a system for l2 teaching and learning, to tatar-a low-resource turkic language, spoken by over 7m people. it describes in some detail the constructs implemented to date. to the best of our knowledge, this is the first work dedicated to the development of l2 teaching and learning tools specifically for tatar at the intermediate to advanced levels, based on state-of-the-art technologies available at present. work is on-going on identifying constructs most useful for teaching, and classifying them by proficiency levels, in accordance with the scale of assessment of language competencies. the next major development phase is to extend the inventory of constructions to include more complex syntactic constructs, with a particular interest in synthetic subordinate clauses @xcite . we are also working on expanding the selection of texts on various topics to create a more complete open library, to give learners who do not have a source of their own texts a wider choice from the public library. having more texts will increase the amount of content for training ai models-for example, using transfer learning from turkisha related, higher-resource language-which may help address problems of low-resource languages. an essential system component, found to be extremely useful in the development of other languages, is a syntactic dependency parser. such a parser (of reasonable quality) is not available for tatar at present. when one becomes available, the quality of the analysis-and the variety and quality of the automatically generated exercises-will progress to the next level. further work will focus on developing the platform to support tatar. the ultimate goal is to bring the tatar revita to a level of functionality that can be deployed for teaching and learning tatar, e.g., in schools with teachers, as is done for other languages for which richer resources are availablecurrently finnish and russian. we also hope that this work will contribute to stimulating global interest in the study and development of tatar-and other low-resource languages in need of supportusing the latest nlp technologies and theories of l2 acquisition.