Professionals · code 2310

University and higher education teachers

AI can mark the exam and draft the paper. Sparking an original idea in a student is still teaching.

Marking exams and writing academic papers are where AI could take a fair chunk. Supervising practicals is mostly out of its reach, and actually giving lectures and tutorials it barely touches.

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

University and higher education teachers prepare and deliver lectures and conduct tutorials in one or more subjects within a prescribed course of study at a university or other higher educational institution. They conduct research, and prepare scholarly papers and books. Tasks include - (a) designing and modifying curricula and preparing courses of study in accordance with requirements; (b) preparing and delivering lectures and conducting tutorials, seminars and laboratory experiments; (c) stimulating discussion and independent thought among students; (d) supervising, where appropriate, experimental and practical work undertaken by students; (e) administering, evaluating and marking examination papers and tests; (f) directing research of postgraduate students or other members of department; (g) researching into and developing concepts, theories and operational methods for application in industrial and other fields; (h) preparing scholarly books, papers or articles; (i) participating in departmental and faculty meetings and in conferences and seminars. Examples of the occupations classified here: - Higher education lecturer - Professor - University lecturer - University tutor Some related occupations classified elsewhere: - University chancellor - 1120 - Dean (university)-1345 - Higher education faculty head - 1345 - Vocational education teacher - 2320

social work practice educator
higher education lecturer

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

63 / 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

295 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 9 tasks for how much of it AI could do. They average 36.9 out of 100, so it holds up at 100 − 36.9 = 63.1.

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 20.4 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?

University and higher education teachers 63

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 feedback.
Drafting papers and literature reviews.
Course materials.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

Lectures, seminars and discussion.
Supervising research.
Original ideas and judgement.

Both

What's arriving

New work that only exists because the AI does.

Assessment that AI can't do for students.
Research using AI tools.
Teaching students to use AI critically.

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

46 could do without you

It could take over some of the work, and help with more. It could help with 70 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Lower

20 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 9 out of 100. The higher it is, the longer the robot waits.

20 How
hands-on

9 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 35 US jobs , so treat it as rough.

finger skill 14.5 · hand skill 5 · cramped spaces 6.7 · 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 56, against an average of 37. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

56
Administering, evaluating and marking examination papers and tests

Medium
54
Preparing scholarly books, papers or articles

Medium
44
Researching into and developing concepts, theories and operational methods for application in industrial and other fields

Low
36
Designing and modifying curricula and preparing courses of study in accordance with requirements

Low
35
Directing research of post-graduate students or other members of department

Low
29
Participating in departmental and faculty meetings and in conferences and seminars

Low
28
Stimulating discussion and independent thought among students

Low
28
Supervising, where appropriate, experimental and practical work undertaken by students

Low
23
Preparing and delivering lectures and conducting tutorials, seminars and laboratory experiments

Very Low

9 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: 70
AI does it for you: 46

Sidekick wins, by 24 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 2421, 2422

So, should university lecturers be worried?

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

Parts of the work are changing. Marking exams and writing papers are rated Medium; research, curricula, supervision, meetings, discussion and practical work are Low; lecturing rates Very Low. AI affects assessment and writing most, so universities are rethinking both.

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 →