Routledge Handbook of AI and World Language Learning – call for chapters

Editors: Weixiao Wei 

The Routledge Handbook of AI and World Language Learning will explore the transformative role of artificial intelligence in language education, offering a critical and comprehensive analysis of how AI is expected to reshape the ways languages are taught, learned, and assessed.

Preliminarily divided into six thematic sections, the handbook will bridge theory, research, and practice to establish AI-driven language learning as a rigorous academic field. It is intended to serve as a vital resource for researchers, educators, ed-tech developers, policymakers, and postgraduate students.

The section and chapter titles listed below are intended as illustrative examples. Contributors are invited to adopt similar titles or propose new ones that reflect their individual research and expertise. We particularly encourage exploration of world language learning beyond, though not excluding, English.

Section 1: AI and Language Learning

1. Foundations of AI in Language Learning
2. AI-Powered Language Learning Applications
3. Machine Learning and Second Language Acquisition
4. AI and Personalized Language Learning Pathways
5. Adaptive Learning Systems in Language Education
6. The Role of AI in Informal Language Learning
7. AI and Language Learning Motivation
8. Virtual Reality and AI in Language Learning
9. AI and Multilingual Education
10. Ethical Considerations in AI-Enhanced Language Learning

Section 2: AI and Language Teaching

1. AI as a Teaching Assistant: Opportunities and Challenges
2. AI and Automated Language Instruction
3. AI-Driven Feedback Mechanisms in Language Teaching
4. AI and Data-Driven Language Pedagogy
5. AI for Teacher Training and Professional Development
6. Chatbots and Conversational AI in Language Instruction
7. AI-Integrated Blended Learning Approaches
8. AI in Curriculum and Lesson Planning
9. Gamification and AI in Language Teaching
10. AI in Language Teacher Education Programs

Section 3: AI and the Context of Language Learning and Teaching

1. The Socio-Cultural Implications of AI in Language Learning
2. AI and Language Learning in Multicultural Contexts
3. The Role of AI in Language Policy and Planning
4. AI and the Digital Divide in Language Learning
5. AI and Equity in Language Education
6. Ethical AI and Bias in Language Learning Technologies
7. AI and Language Learning for Migrants and Refugees
8. AI and Accessibility in Language Learning
9. The Role of AI in Preserving Endangered Languages
10. Future Directions in AI and World Language Education

Section 4: AI and Language Testing and Assessment

1. AI in Language Testing: An Overview
2. Automated Essay Scoring and
3. AI-Based Speech Recognition for Language Assessment
4. Adaptive AI-Driven Language Testing
5. AI and Standardized Language Proficiency Exams
6. AI for Diagnostic and Formative Assessment
7. Bias and Fairness in AI-Driven Language Testing
8. AI for Feedback and Error Correction in Assessment
9. The Role of AI in High-Stakes Language Testing
10. AI and Blockchain in Secure Language

Section 5: AI, Future Trends, and Policy in Language Education

1. AI and the Future of Language Learning
2. AI, Language Learning Analytics, and Big Data
3. AI and Ethical Considerations in Educational Policy
4. AI-Powered Virtual Teachers and Tutors
5. The Role of Governments and Institutions in AI and Language Education
6. AI for Lifelong Language Learning and Workplace Training
7. AI and Personalized Learning Models in Language Education
8. AI and Collaboration Between Human and Machine Intelligence
9. The Role of AI in Enhancing Intercultural Competence
10. Research Agenda for AI and World Language Learning

Section 6: Developing AI Programs for Language Learning

1. Designing AI-Powered Language Learning Systems
2. Natural Language Processing (NLP) in Language Learning
3. Building Adaptive Language Models for Individual Learners
4. AI for Pronunciation and Speech Recognition
5. Machine Learning Techniques for Vocabulary Acquisition
6. Gamification and AI: Enhancing Engagement in Language Learning
7. AI-Powered Writing Feedback Systems
8. Emotional AI in Language Learning: Tracking Learner Sentiment
9. Data-Driven Insights for Personalized Learning Paths
10. Ethical Considerations in Developing AI for Language Education

Submission Guidelines:

• Proposals should be submitted as an abstract (200-300 words) outlining the main argument, scope, and structure of the chapter.
• The submission should include a brief biography (50-100 words) of the author(s), highlighting relevant expertise. Maximum Numbers of authors allowed per chapter: 3.
• Proposals should be submitted as soon as possible and no later than May 15, 2025.
• Full chapters (7,000-9,000 words) will be expected by March 1, 2026.

Please send all inquiries and submissions to Dr Weixiao Wei at wwei21@cougarnet.uh.edu