Compare roles · AI exposure side by side
University Lecturers and Professors
Put up to three roles next to each other: the exposure figure, the inputs behind it, the window, what is driving change, and where the work moves. Every figure comes from the same method, so the gap between two scores means something.
Exposure
Window · adoption
until most of the change has landed
High adoption
In one line
AI drafts lecture materials, grades and gives feedback, summarises literature and handles admin, while teaching judgement and mentoring stay human.
Inputs behind the score · scaled 0–100
Task applicability
64Observed usage
41Official exposure tier
100Labour-market trajectory
n/aPublished adoption rating
70What is driving it
- Student adoption
- Institutional licences
- AI-assisted grading and feedback
- Content generation
- Financial pressure on institutions
- Online and hybrid programmes
Skills worth building
- Assessment design
- Discipline-specific AI literacy
- Research supervision and mentoring
- Prompting and tool fluency
- Scholarly judgement and originality
- Facilitation and discussion leadership
Tools in the work now
Where the work moves
Full report
Scores are the CareerGuard Exposure Index v2: the same five inputs and weights for every role, so a gap of ten points means the same thing wherever it appears. How scores are built.