An overview of artificial intelligence in computer-assisted language learning
Project Overview
The document explores the transformative role of generative AI in computer-assisted language learning (CALL), addressing the growing necessity for intelligent agents amid a shortage of human educators and the rise of distance learning. It emphasizes the key applications of AI in language education, particularly through Intelligent Tutoring Systems (ITS), which offer personalized learning experiences, automate exercise generation, and deliver customized feedback to learners. The advancements in machine learning, particularly large language models (LLMs), are highlighted for their significant potential to enhance the effectiveness and efficiency of CALL systems, ultimately fostering improved outcomes in language acquisition and teaching methodologies. The integration of these AI technologies aims to provide scalable, adaptive, and responsive educational tools that meet the evolving demands of learners and educators alike.
Key Applications
Intelligent Tutoring and Assessment Systems
Context: Language learning for university-level students and adult learners, including writing proficiency assessment and speaking practice.
Implementation: Deployment of AI-driven systems like Cognitive Tutor, ALEKS, and Automatic Essay Scoring (AES) systems, along with dialogue systems and automated exercise generation tools. These systems utilize machine learning techniques to adapt to individual learner needs, provide immediate feedback, generate tailored exercises, and facilitate conversational practice.
Outcomes: Improved learning outcomes and engagement; students demonstrate greater performance gains compared to traditional methods, receive immediate feedback on writing quality, and experience reduced speaking anxiety through interactive practice.
Challenges: High resource requirements for developing complete systems; ensuring the accuracy of scoring and quality of generated content; limited access to fully functioning prototypes; need for interpretability in feedback; lack of nuanced emotional engagement.
Implementation Barriers
Resource-related
High costs and resource demands for developing comprehensive AI systems.
Proposed Solutions: Development of scalable prototypes and leveraging open-source resources.
Quality assurance
Challenges in ensuring the quality and relevance of generated educational content.
Proposed Solutions: Incorporating expert reviews and iterative testing of generated materials.
Technological limitations
Issues related to the accuracy and interpretability of automatic feedback mechanisms.
Proposed Solutions: Continuous improvement of AI models and integration of user feedback mechanisms.
Project Team
Anisia Katinskaia
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Anisia Katinskaia
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai