Compare roles · AI exposure side by side
University Lecturers and ProfessorsvsTutors
Tutors is 3 points more exposed than University Lecturers and Professors (71 against 68).
Exposure
Window · adoption
until most of the change has landed
High adoption
until most of the change has landed
Very 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.
AI tutors now explain concepts, mark practice work and adapt to each learner on demand, pushing human tutors toward motivation, accountability and harder cases.
Inputs behind the score · scaled 0–100
Task applicability
64Observed usage
41Official exposure tier
100Labour-market trajectory
n/aPublished adoption rating
70Task applicability
58Observed usage
54Official exposure tier
100Labour-market trajectory
n/aPublished adoption rating
85What is driving it
- Student adoption
- Institutional licences
- AI-assisted grading and feedback
- Content generation
- Financial pressure on institutions
- Online and hybrid programmes
- Conversational AI tutors
- Subject-specific apps
- Platform integration
- Student-led adoption
- Cost pressure on families and schools
- Adaptive learning data
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
- Diagnostic teaching
- Motivation and coaching
- AI tool fluency
- Special needs expertise
- Exam and curriculum knowledge
- Parent and student communication
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.