Routledge Handbook of AI and World Language Learning
Routledge Handbook of AI and World Language Learning – call for chapters
Editors: Weixiao Wei and Chris Shei
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
11. AI as a Teaching Assistant: Opportunities and Challenges
12. AI and Automated Language Instruction
13. AI-Driven Feedback Mechanisms in Language Teaching
14. AI and Data-Driven Language Pedagogy
15. AI for Teacher Training and Professional Development
16. Chatbots and Conversational AI in Language Instruction
17. AI-Integrated Blended Learning Approaches
18. AI in Curriculum and Lesson Planning
19. Gamification and AI in Language Teaching
20. AI in Language Teacher Education Programs
Section 3: AI and the Context of Language Learning and Teaching
21. The Socio-Cultural Implications of AI in Language Learning
22. AI and Language Learning in Multicultural Contexts
23. The Role of AI in Language Policy and Planning
24. AI and the Digital Divide in Language Learning
25. AI and Equity in Language Education
26. Ethical AI and Bias in Language Learning Technologies
27. AI and Language Learning for Migrants and Refugees
28. AI and Accessibility in Language Learning
29. The Role of AI in Preserving Endangered Languages
30. Future Directions in AI and World Language Education
Section 4: AI and Language Testing and Assessment
31. AI in Language Testing: An Overview
32. Automated Essay Scoring and
33. AI-Based Speech Recognition for Language Assessment
34. Adaptive AI-Driven Language Testing
35. AI and Standardized Language Proficiency Exams
36. AI for Diagnostic and Formative Assessment
37. Bias and Fairness in AI-Driven Language Testing
38. AI for Feedback and Error Correction in Assessment
39. The Role of AI in High-Stakes Language Testing
40. AI and Blockchain in Secure Language
Section 5: AI, Future Trends, and Policy in Language Education
41. AI and the Future of Language Learning
42. AI, Language Learning Analytics, and Big Data
43. AI and Ethical Considerations in Educational Policy
44. AI-Powered Virtual Teachers and Tutors
45. The Role of Governments and Institutions in AI and Language Education
46. AI for Lifelong Language Learning and Workplace Training
47. AI and Personalized Learning Models in Language Education
48. AI and Collaboration Between Human and Machine Intelligence
49. The Role of AI in Enhancing Intercultural Competence
50. Research Agenda for AI and World Language Learning
Section 6: Developing AI Programs for Language Learning
51. Designing AI-Powered Language Learning Systems
52. Natural Language Processing (NLP) in Language Learning
53. Building Adaptive Language Models for Individual Learners
54. AI for Pronunciation and Speech Recognition
55. Machine Learning Techniques for Vocabulary Acquisition
56. Gamification and AI: Enhancing Engagement in Language Learning
57. AI-Powered Writing Feedback Systems
58. Emotional AI in Language Learning: Tracking Learner Sentiment
59. Data-Driven Insights for Personalized Learning Paths
60. 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 June 1, 2025.
Full chapters (7,000-9,000 words) will be expected by April 1, 2026.
Please send all inquiries and submissions to Weixiao Wei at wwei21@cougarnet.uh.edu copying in Chris Shei at C-C.Shei@Swansea.ac.uk or ccshei@gmail.com.
Dr. Chris Shei
