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Professionals · code 2353

Other language teachers

AI can translate and correct your homework. Speaking with confidence still takes practice with a person.

Marking and making teaching materials are where AI could take a fair chunk. Teaching the lesson is mostly out of its reach, and helping students in classes taught in another language it barely touches.

What they actually do (and what else they get called)

Other language teachers teach non-native languages to adults and children who are learning a language for reasons of migration, to fulfil employment requirements or opportunities, to facilitate participation in educational programmes delivered in a foreign language, or for personal enrichment. They work outside the mainstream primary, secondary and higher education systems, or in support of students and teachers within those systems. Tasks include - (a) assessing the level of ability and the extent of language difficulties of students, and establishing students’ needs and learning goals; (b) planning, preparing and delivering lessons and workshops for groups and individuals, with content and rate of progression adapted to students’ abilities and needs; (c) designing and producing teaching materials and adapting existing materials; (d) assessing students’ progress; (e) assisting students in classroom settings where subjects are taught in a language other than the students’ native language; (f) providing assistance to other teachers by designing special teaching programmes for students still learning the main language of instruction; (g) assigning and correcting work, and preparing and grading exams; (h) assessing, recording and reporting on students’ progress. Examples of the occupations classified here: - Intensive language teacher - Migrant education teacher - Practical language teacher - Second language teacher Some related occupations classified elsewhere: - University lecturer - 2310 - Vocational education teacher - 2320 - Secondary school teacher - 2330 - Primary school teacher - 2341

  • language school teacher
  • sign language teacher

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

66 / 100

Mostly holds up against AI

AI can take a few chores off your plate, but most of the day is still yours.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Minimal Exposure

266 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 8 tasks for how much of it AI could do. They average 33.9 out of 100, so it holds up at 100 − 33.9 = 66.1.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Minimal Exposure”: A few tasks are affected, but the heart of the job isn't.

This job rates 21.5 out of 100 for how hands-on it is. The score above only looks at AI tools, not robots, but for work this far from the physical that matters less.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Other language teachers 66

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Marking and exam preparation.
  • Creating teaching materials.
  • Assessing progress.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Conversation practice and confidence.
  • Cultural understanding.
  • Supporting learners who struggle.

Both

What's arriving

New work that only exists because the AI does.

  • Teaching alongside AI tutors.
  • Supporting migrants and refugees.
  • Specialist and business language coaching.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Medium

54 could do without you

It could take over some of the work, and help with more. It could help with 74 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

22 how hands-on

Not much of this job needs hands, so the robot can sit this one out.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 12 out of 100. The higher it is, the longer the robot waits.

22 How
hands-on
12 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 4 US jobs .

finger skill 21.8 · hand skill 10.4 · cramped spaces 5.2 · averaged · rule: hands-on under 50

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 52, against an average of 34. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 52

    Assigning and correcting work, and preparing and grading exams

    Medium
  2. 50

    Designing and producing teaching materials and adapting existing materials

    Medium
  3. 34

    Assessing, recording and reporting on students’ progress.

    Low
  4. 32

    Assessing students' progress

    Low
  5. 29

    Assessing the level of ability and the extent of language difficulties of students, and establishing students' needs and learning goals

    Low
  6. 27

    Providing assistance to other teachers by designing special teaching programmes for students still learning the main language of instruction

    Low
  7. 26

    Planning, preparing and delivering lessons and workshops for groups and individuals with content and rate of progression adapted to students’ abilities and needs

    Low
  8. 24

    Assisting students in classroom settings where subjects are taught in a language other than the students’ native language

    Very Low

8 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
74
AI does it for you
54

Sidekick wins, by 20 points.

How good a match is this?

2 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 2492, 2493

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Out of every 100 conversations

51
49

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job. This job makes up 1.37% of the conversations.

The data is sorted by US job, and this one matches 2 of them. We've combined them, counting each by how often it comes up in the conversations, so the busiest one counts most.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should language teachers be worried?

Short answer: not much. Some of the chores will change, and good riddance.

This is one of the more exposed teaching jobs. Marking and creating materials are rated Medium; assessing and planning lessons are Low. AI tutors and translation tools are strong at language, so teachers focus on conversation, confidence and culture.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 92 jobs in the “Professionals” family, averaging 61. This one is above the family average.

Pit this against any other job →