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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
- NewsEdSurge1 Jul 2026
When the Algorithm Reaches Its Limit, Who Steps In?
A teacher built an AI book recommendation app, LibraryAid, to match students with books, but found that algorithm success still requires teacher intervention for motivational conversations. Another teacher analyzed the instructional time lost enforcing Yondr phone pouches, questioning where technology ends and educator responsibility begins.
Original abstract
<p>What happens when a tool works exactly as designed, and still falls short? <a href="https://www.edsurge.com/author/david-webb"><u>David Webb</u></a>, an international school teacher in Jakarta, India, spent a year building <a href="https://libraryaid.net/"><u>LibraryAid</u></a>, an AI-powered book recommendation app without a computer science background, and discovered precisely where an algorithm ends and an educator begins. <a href="https://www.edsurge.com/author/gabe-nitro"><u>Gabe Nitro</u></a>, a high school teacher in California, watched his school embrace Yondr phone pouches as a solution to classroom distraction and found himself doing the math on how much instructional time the enforcement itself was consuming. Two tools, two classrooms, one question: when the technology reaches its limit, who steps in?</p><h2><strong>A Year of Vibe Coding, One Unexpected Lesson</strong></h2><p>Webb describes the process of building LibraryAid through vibe coding, describing what he wanted in plain English and letting AI tools write the code, spending a year marked with near defeats and small breakthroughs. The app now tracks roughly 30 factors to match students to books in their own school library catalog. One student reading two grade levels behind made three times the average reading progress after the app connected him to a book series he loved. Yet Webb found himself returning, again and again, to a line he wrote in his piece for <strong>EdSurge</strong>: the algorithm had done its job, and what the child needed next was a conversation with a trusted adult. Join us on <strong>This Week with EdSurge</strong> where we ask what Webb learned about reading, motivation, and the irreplaceable role of the teacher in the loop.</p><h2><strong>The Pouch Solved One Problem. Then What?</strong></h2><p>Gabe Nitro is a high school teacher in California whose school uses Yondr pouches to lock students’ phones away for the school day. He is not arguing for phones in classrooms. He i