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University Lecturer

Based on 10 assessments · 1 from real users

22% Low risk

Average realistic automation risk across all University Lecturer profiles in the dataset.

Raw potential
54%
Realistic risk
22%
Research benchmark ?
41%

Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context. Research benchmark: Eloundou et al. (2023)

Distribution across 10 profiles. Middle half of University Lecturers score between 20% and 25%.

0% 50% 100%
p10 · 18%
26% · p90
On-screen work 46%

Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.

In-person + screen 1%

Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.

Computer + action 39%

Computer input, real-world output — needs someone to act on it, not just software.

Fully in-person 14%

No computer required. Furthest from automation — the strongest human advantage.

3 synthetic profiles for a University Lecturer, ordered by automation exposure. Tab between them to see how task mix drives the score difference.

Task Time Type Exposure
Preparing and delivering lectures to students (writing slides, reviewing content, standing lectures)
deep expertise
34% DA 0%
Conducting or supervising research projects and analyzing results
deep expertise social element
27% DD 26%
Attending department meetings, committees, and administrative tasks
14% AA 8%
Grading assignments, exams, and student work; providing written feedback
14% DD 43%
Holding office hours and answering student emails and questions
deep expertise
6% DA 7%
Writing and submitting academic papers, grant proposals, and research reports
1% DD 68%
Mentoring graduate students and supervising theses
deep expertise
1% DA 0%

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