language-assessment
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- PaperIELTS Partnership Research Reports
An investigation of the language assessment interests and needs of professional registration bodies in the UK: An unconsidered perspective?
This study explores the language assessment interests and needs of professional registration bodies in the UK, highlighting a previously overlooked perspective in the field.
- PaperIELTS Partnership Research Reports
Comparing New TOEFL 2026 with Former TOEFL 2023 and IELTS
This paper compares the upcoming TOEFL 2026 test with its 2023 predecessor and the IELTS, highlighting key differences in structure and scoring.
- PaperOpenAlex — TESOL researchForthcoming · 1 Dec 2026
Future of AI in English Language Education: Trends and Predictions
Prof. Jagadeesh Nerlekar, K Munianjinappa
This paper synthesizes current AI developments in English language education, identifying key trends such as personalized feedback, adaptive pathways, automated formative assessment, realistic speaking/listening practice via conversational agents, and AI-assisted material creation. It predicts that hybrid systems combining large language models with pedagogical scaffolding and teacher mediation will have the most near-term impact. The paper also addresses challenges including bias, privacy, over-reliance on automation, and the need for teacher training, offering recommendations for educators and policymakers.
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
- PaperApplied Linguistics24 Jul 2026
INTRODUCING SECOND LANGUAGE ASSESSMENT
Lin Shi, Lianzhen He
An introduction to second language assessment is provided, covering key concepts and approaches.