AI for Teaching Languages: Activities, Feedback, and Limits

AI can help language and ESL teachers design activities, draft feedback comments, and adapt materials to different proficiency levels faster than working from scratch each time. The gains are real, and the limits matter just as much: a model that has never met your learners cannot judge their readiness, choose the right vocabulary ceiling for the class, or replace the teacher's sense of what fell flat last Tuesday.

A tool like fenbs keeps your tested prompts and teaching decisions in one place, so the next time you open an AI app, whether it's the same one or a different one, you start from what already worked.

What AI is good at in a language classroom

The most useful jobs for AI in language teaching are the ones that eat preparation time without requiring classroom judgment. Generating a first draft of a reading passage at a target CEFR level, producing ten vocabulary-in-context sentences from a word list, or rewriting a paragraph at a simpler level are all tasks a model handles quickly and that a teacher can edit rather than write from zero.

AI is also useful for writing feedback frames: sentence starters or comment banks tied to a specific learning objective. Say a B1 class is working on connecting ideas with discourse markers. You can ask for a bank of short feedback phrases for that goal, then use and edit them across a set of papers rather than writing each comment twice.

What AI cannot do reliably is assess spoken fluency without a purpose-built tool, judge whether an activity fits your school's pacing guide, or produce a final assessment without human review. Use it for generation, not for final grading decisions.

Adapting activities to proficiency levels

The most common request language teachers make of AI is: "Make this easier" or "Make this harder." Results improve when you give the model the CEFR level, the specific language feature you are targeting, and the format you want (gapped sentences, true/false, matching). Vague instructions produce vague output.

• State the level: A2, B1, B2, or a US-grade equivalent. "Elementary" means different things to different people.
• Name the target feature: past perfect, reported speech, hedging language, phrasal verbs with "take".
• Give the format: controlled practice, guided writing, open discussion prompt.
• Include context: topic, text length, any vocabulary to avoid.

A well-specified prompt produces a draft you spend five minutes editing. A vague prompt produces a draft you spend thirty minutes rewriting. Save the well-specified prompts; they are worth keeping.

ESL-specific workflows

ESL teachers often work with learners whose first languages create predictable error patterns. If you teach Spanish speakers learning English, you already know the articles and ser/estar interference are constant. Ask the model for practice activities that target those patterns specifically, not activities designed for generic English learners.

For academic ESL work, AI is useful for generating corpus-informed vocabulary lists from a text, writing glosses for difficult words, or producing a simplified version of an academic article for a lower-level class. Always check that simplified output has not changed the meaning of technical terms the learners need to understand accurately.

Pronunciation and speaking practice benefit less from text-based AI, though you can use it to write conversation scripts, discussion cards, and debate prompts. Purpose-built tools are better for pronunciation feedback. For AI that supports broader language learning workflows, see the note on AI for language learning workflows.

Writing feedback that helps learners improve

Feedback is where AI can save significant time, but only if the output is genuinely tied to the learner's text. Pasting in a student's paragraph and asking for generic comments produces generic comments. Pasting in the paragraph with the rubric criterion, the level, and the learning objective produces something worth editing.

A practical workflow: draft the feedback with AI, read it against the student's work, remove any comment that doesn't match what the student actually wrote, and add anything the model missed. The teacher's reading of the text is irreplaceable; the model speeds up the first draft.

Be cautious about asking a model to score or grade work. Scoring requires consistent application of criteria across a set, and models do not apply criteria consistently across multiple independent requests. Use AI for comments, not scores.

Using AI to design assessments and rubrics

Rubric design is a strong use case. Give the model the learning objective, the task type, the proficiency level, and the number of criteria you want. Ask it to produce a draft rubric with descriptor language for each performance band. Review it against your course outcomes and the standards your school uses, such as ACTFL or WIDA for US language programs.

For speaking and listening assessments, AI can generate question sets, scenario cards, and listening comprehension tasks faster than typing them out. It can also produce multiple versions of a reading test at the same difficulty level, which is useful for retakes. Check every assessment for cultural assumptions that may disadvantage particular learners.

Teaching decisions that should outlast a single session

Language teachers accumulate knowledge about what works: which activity format a particular class responds to, which vocabulary approach a department has agreed on, which assessment standard the school follows. This knowledge is often in one teacher's head or in a shared drive that nobody opens.

When you record a teaching decision, "We agreed not to use translation activities in the first two units," as a rule in an AI-connected tool, the next session starts with that constraint. You don't explain it again. The AI app you use reads the rule and applies it.

How to do this with fenbs

1. Create a free account and open your personal board. Add a project for the course or class you prepare most.
2. Write your first rule: a teaching decision that applies consistently, such as the proficiency level you're targeting or a department agreement about vocabulary coverage.
3. Save a prompt that worked. Add it to Skills and prompts with a short title ("B1 reading passage, discourse markers, 250 words") so you can reuse it.
4. Connect an AI app from Integrations. Before your next lesson prep session, ask the AI to read the project context and your rules.
5. After each session, note what worked and what didn't. A lesson that flopped at B1 is a lesson: record it so the next prep session starts with that adjustment already in place.

Where AI has clear limits for language teachers

AI-generated texts may contain culturally specific references that are confusing or inappropriate for particular learner groups. They may include vocabulary above the stated level, idiomatic language that is off-register for academic ESL, or subtle errors in target language that a native speaker would notice but a model would not flag.

Always read generated materials before using them in class. A five-minute check catches most problems. The teacher's professional judgment about what a class needs on a given day is not something an AI can replace, because it depends on relationships and observations the model has no access to.