AI in Language Teaching
Methodology
Artificial intelligence is transforming English language teaching through automated writing feedback, conversational chatbots, adaptive learning platforms, AI-generated materials, and machine translation as a learning tool. The field is evolving rapidly — applications that were experimental in 2020 became mainstream by 2025.
Current Applications
| Application | What it does | Examples |
|---|---|---|
| Automated Writing Evaluation (AWE) | Analyses writing for grammar, vocabulary, coherence, and task achievement | Grammarly, Write & Improve, ETS e-rater |
| Conversational AI / Chatbots | Provides speaking and writing practice through dialogue | ChatGPT, Replika, language-specific bots |
| Adaptive learning platforms | Adjusts content difficulty and pacing based on learner performance | Duolingo, ELSA Speak, Busuu |
| AI-generated materials | Creates exercises, reading passages, listening scripts, and test items | LLM-based content generation |
| Machine translation | Supports comprehension and raises language awareness | Google Translate, DeepL |
| Speech recognition | Provides pronunciation feedback | ELSA Speak, SpeechAce |
| Learning analytics | Tracks learner behaviour and predicts outcomes | LMS-integrated analytics |
Research Evidence
Meta-analytic evidence (Lyu 2025) shows AI chatbots have a medium positive effect on L2 learning (g = 0.608). Key findings:
- Chatbots reduce speaking anxiety by providing a low-stakes practice environment
- Automated feedback is most effective when combined with teacher feedback, not as a replacement
- Lower-proficiency learners may benefit more from AI interaction than advanced learners
- Task design matters more than the technology itself — AI tools need purposeful integration
Opportunities
- Scalability — AI provides individualised feedback and practice that would be impossible for a single teacher to deliver at scale
- Accessibility — 24/7 availability; learners can practise outside class time
- Personalisation — adaptive systems respond to individual learner needs, pace, and level
- Efficiency — automated assessment frees teacher time for higher-value interactions
- Data-driven insight — learning analytics can inform pedagogical decisions
Concerns and Limitations
- Accuracy — AI feedback can be unreliable, especially for pragmatic, discourse-level, and culturally specific language use
- Overreliance — learners may develop dependency on AI tools rather than building autonomous competence
- Equity — access to AI tools requires devices, internet, and often subscriptions
- Academic integrity — AI-generated text complicates assessment of genuine learner production
- Data privacy — learner data fed to AI platforms raises ethical questions
- Teacher deskilling — if AI automates feedback and materials creation, teachers may lose professional competencies
- Bias — AI models reflect training data biases, potentially reinforcing native-speaker norms and cultural assumptions
Pedagogical Principles
Effective AI integration follows the same principles as any TELL:
- Start with learning objectives, not the technology
- Use AI to complement — not replace — teacher expertise
- Train learners to evaluate AI output critically
- Maintain human connection and communicative authenticity
- Address equity and access proactively
Key References
- Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning & Technology, 26(2), 5–24.
- Lyu, B. (2025). Effectiveness of chatbots in improving language learning: A meta-analysis. International Journal of Applied Linguistics, 35(1).
- Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550.