AI-Integrated Language Learning Transforming Pedagogical Paradigms in ELT
Bibliographic record
Abstract
This chapter critically scrutinizes the AI integration into English Language Teaching (ELT) from the perspective of critical CALL and explores AI as a sociotechnical matter that transforms pedagogical relationships, teacher agency, and ethical reflections in language education. Drawing on recent empirical studies and international case studies from three different cultural backgrounds, this chapter reports on the overall positive or negative perceptions and experiences of applying AI tools in ELT, as well as global practices for implementing AI tools. Our Curated Augmentation Framework for AI serves as a support role, rather than a replacement model, to enable AI as an aid for enhancing—not replacing—a human pedagogy, whether for foundational skills, formative assessment, or extended learning. Ethical issues, such as algorithmic bias, linguistic justice, and cultural adaptation, are introduced, along with practical suggestions for teacher agency. The chapter concludes with future research agendas designed to ensure the adoption of AI for the humanistic core of language learning, particularly in under-researched contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".