ai-in-language-education
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- PaperOpenAlex — TESOL researchForthcoming · 1 Dec 2026
Future of AI in English Language Education: Trends and Predictions
Prof. Jagadeesh Nerlekar, K Munianjinappa
The paper synthesizes current AI developments in English language education and predicts hybrid systems combining large language models with pedagogical scaffolding will have the greatest near-term impact. Key trends include personalized feedback, automated assessment, multimodal practice, and AI-assisted materials, while challenges involve bias, privacy, and teacher training. Recommendations emphasize human-AI workflows, explainability, data ethics, and teacher capacity building.
Original abstract
Artificial intelligence (AI) is transforming English language education (ELE) by enabling personalized learning, automated assessment, adaptive content generation, and immersive practice environments. This paper synthesizes current developments, identifies emergent trends, and offers evidence-informed predictions about how AI will shape classroom practice, curriculum design, assessment, teacher roles, and policy over the next decade. Drawing on interdisciplinary literature from computer-assisted language learning (CALL), intelligent tutoring systems (ITS), natural language processing (NLP), and educational policy, the paper argues that the most significant near-term impact will stem from hybrid systems that combine large language models (LLMs) with pedagogically informed scaffolding and teacher mediation. Key trends discussed include (1) ubiquitous personalized feedback and adaptive pathways; (2) automated, formative assessment with rich analytics; (3) realistic speaking/listening practice via multimodal conversational agents and immersive virtual environments; (4) AI-assisted material creation and differentiation for diverse learner needs; and (5) data-driven teacher support and professional development. Predictions address likely improvements in scalability and access, as well as persistent challenges: bias and fairness in language models, privacy and data governance, over-reliance on automated feedback, and the need for robust teacher training and curricular alignment. The paper concludes with practical recommendations for educators, institutions, and policymakers to harness AI’s affordances while safeguarding equity, transparency, and pedagogical quality. These include adopting hybrid human–AI workflows, emphasizing explainability and interpretability in tools, developing clear data-ethics policies, investing in teacher capacity building, and prioritizing research-practice partnerships. The analysis aims to be actionable for practitioners and decision-makers planning for an AI-augmented future of English language learning. Keywords: artificial intelligence, English language education, adaptive learning, large language models, assessment, teacher role, ethics
- PaperOpenAlex — TESOL research22 Jul 2026
AI in language education
Jake Cummings
AI tools in second language acquisition promise efficiency and personalization but are often disconnected from authentic learning and equity. Teacher preparation programs lag behind, leaving educators underprepared. This chapter analyzes AI through Communities of Inquiry and Second Language Acquisition frameworks, finding that educational value depends on teacher digital competence and critical use.
Original abstract
AI tools are increasingly embedded in second language acquisition (SLA), yet their integration raises unresolved tensions between technological potential and pedagogical practice. While AI applications promise efficiency, personalization, and extended opportunities for interaction, these affordances are often overstated or disconnected from authentic learning, cultural exchange, and equity. At the same time, teacher preparation programs have been slow to adapt, leaving teachers underprepared to critically evaluate AI-mediated language learning environments. This chapter examines AI in SLA through the interpretive frameworks of the CoI and SL2, positioning teacher digital competence as a mediating construct between technological capability and educational value. Traditional SLA approaches provide historical context, while adaptive systems, chatbots, and GenAI are analyzed in terms of presence, interaction, authenticity, and cultural depth. Three themes emerge: Expanded opportunities for cognitive and social presence, persistent risks related to inequity, and the central role of teacher judgment. The chapter concludes that the educational value of AI depends less on technical innovation than on the preparedness of teachers to use it critically, ethically, and inclusively to design and mediate AI-supported language learning experiences.