An overview of artificial intelligence in computer-assisted language learning

TL;DR

The paper addresses how artificial intelligence can augment computer-assisted language learning by enabling intelligent tutoring systems that scale language teaching tasks. It surveys AI methods for language learning from a CALL developer perspective, focusing on the ITS architecture (Domain, Student, and Instruction models), exercise generation, and assessment and feedback, with emphasis on reading, listening, and speaking activities and the role of large language models and transformers. It notes that most CALL systems remain prototypes or partial implementations due to resource constraints, and that comprehensive surveys of AI in CALL have been scarce. The work highlights the need to bridge multiple disciplines and provides a developer-oriented viewpoint with future directions, including modeling learner proficiency levels, readability and text simplification, and data/resource creation through learner interactions and crowd-sourcing.

Abstract

Computer-assisted language learning -- CALL -- is an established research field. We review how artificial intelligence can be applied to support language learning and teaching. The need for intelligent agents that assist language learners and teachers is increasing: the human teacher's time is a scarce and costly resource, which does not scale with growing demand. Further factors contribute to the need for CALL: pandemics and increasing demand for distance learning, migration of large populations, the need for sustainable and affordable support for learning, etc. CALL systems are made up of many components that perform various functions, and AI is applied to many different aspects in CALL, corresponding to their own expansive research areas. Most of what we find in the research literature and in practical use are prototypes or partial implementations -- systems that perform some aspects of the overall desired functionality. Complete solutions -- most of them commercial -- are few, because they require massive resources. Recent advances in AI should result in improvements in CALL, yet there is a lack of surveys that focus on AI in the context of this research field. This paper aims to present a perspective on the AI methods that can be employed for language learning from a position of a developer of a CALL system. We also aim to connect work from different disciplines, to build bridges for interdisciplinary work.