Teaching assistant/Tutor in university
AI displacement risk
13%
Range 7.56–17.92%
Teaching assistant/Tutor in university has 62% AI task overlap but 75% human bottleneck protection — lower risk than 53% of occupations in the live market. AI is more likely to enhance this role than replace it.
Professionals·SGD 5,800/mo (4,500–6,000)·~16.1K workers in SG·Updated 2026-06-11
Structural pressure, not a prediction of job loss. Displacement tends to arrive through slower hiring, wage compression and role redesign before layoffs.
Why This Score
62% of tasks overlap with current AI
75% human advantage from judgment & presence
33% demand buffer from the local labour market
AI usage 8pp below theoretical exposure
These factors interact with each other — the final score is not a simple sum of these bars.
How much AI overlaps with this job's tasks, offset by human advantages and local demand. Score stability: watch. How this works
Tasks AI can handle
With 62% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Teaching assistant/Tutor in university tasks most exposed include: generating lesson plans, creating quizzes and practice exercises, summarizing curricula, personalizing reading lists, and grading objective assessments.
- • Develop teaching materials, such as syllabi, visual aids, answer keys, supplementary notes, or course Web sites.
- • Prepare or proctor examinations.
- • Teach undergraduate-level courses.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 75% human bottleneck protection, the tasks that remain hardest to automate for Teaching assistant/Tutor in university include: motivating students, adapting to emotional and social dynamics in the classroom, mentoring, handling behavioral issues, and assessing creative or nuanced work.
Main insulation channels: Deep preparation + Non-routine work — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Eloundou GPT Exposure (Science, 2024), ILO GenAI (2025), Pizzinelli et al. bottleneck model. Full methodology.
Singapore Now
Current labour market conditions and how they affect this role.
Cooling, but not collapsing. Vacancies and re-entry are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
3.1%
↓ 3.1% YoY
Hiring
1.5%
vs 0.9% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
67.7%
find work in 12mo· -5.3pp
Professionals, Managers, Executives & Technicians · 2025 Q4
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Teaching assistant/Tutor in university has some offset potential, but it depends on task redesign holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Similarity-basedCompare within Professionals
See how this compares to similar occupations
Compare with... →Classification
Higher risk than 47% of occupations · V7 structural score
Raw scores
AIOE 1.349 · θ 0.713 · C-AIOE 1.043
Stability
watch · Optimistic 10% · Pessimistic 17%
Score range (best/worst case)
Exposure 43–80% · Net risk 7.56–17.92%
Scoring basis
V7 structural score. Uses task-concentration-weighted exposure, human bottleneck, and demand resilience. V6 baseline fields are retained for release-to-release comparison.-0.7pp versus retained V6 baseline.
Wage range (SGD/mo)
25th 4,500 · Median 5,800 · 75th 6,000
Evidence & sources
Data matching
direct · SSOC 23103
Real-world AI usage: -8% vs estimated
Data quality
80% · Matching 1.00 · Market data 0.70 · Freshness 0.84
100% weighted task match · 12% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Conflicting data signals
Tools & offset factors
What helps
- A meaningful share of the work can likely be reorganized around AI rather than removed outright.
What could slow it down
- Current demand support is thin, so offsets may take longer to show up.
Worker profile & local context
- Vacancy rate is 3.1% and was essentially flat versus last quarter.
- Hiring read: recruitment is running above resignation (1.5% vs 0.9%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 67.7% of retrenched workers re-entered employment within 12 months.
Worker profile
Gender mix
34% male / 66% femalePublished Singapore worker composition for the detailed occupation family 23 Teaching & Training Professionals.
Employment structure
Employee-heavy96% employees, 4% employers or self-employed workers.
Work arrangement
Mostly full-time4% part-time and 96% full-time in 2025.
Age profile
Mid-career heavy14% aged 15 to 29, 62% aged 30 to 49, and 24% aged 50 or older.
Qualification mix
Degree-heavyDegree 81%; Diploma / professional qualification 15%.
Where this work is concentrated
Top planning areas
Sengkang, Bedok, Tampines19% of workers in this occupation group live in these three planning areas.
Residential concentration
Broadly distributed30% live across the top five planning areas in the 2020 Census.
Commute pattern
Mid-range commutesEstimated average commute 37.5 minutes. 33% take 46 minutes or more.
Role profile
How this role's work breaks down across key dimensions. This is a general profile, not an individual measurement.
Workflow dimensions (0 = low, 1 = high)
How this changes by career stage
Senior workers benefit from institutional knowledge and judgment that AI cannot replicate. Entry-level roles have higher task overlap with AI.
Frequently asked questions
Will AI replace Teaching assistant/Tutor in university?
Teaching assistant/Tutor in university has 62% AI task overlap but 75% human bottleneck protection — lower risk than 53% of occupations in the live market. AI is more likely to enhance this role than replace it. Net displacement risk: 13% (Low). Median wage: SGD 5,800/month.
What is the AI risk score for Teaching assistant/Tutor in university?
Teaching assistant/Tutor in university has an AI displacement risk of 13%, rated Low. AI task overlap: 62%. Human advantage: 75%. Local demand buffer: 33%.
What career transitions are available for Teaching assistant/Tutor in university?
Teaching assistant/Tutor in university has modeled transition pathways to related occupations. The strongest adjacent pathway is University lecturer, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Teaching assistant/Tutor in university salary compare in the live market?
Teaching assistant/Tutor in university earns a median gross wage of SGD 5,800/month in the live market (25th-75th percentile: SGD 4,500-6,000). This is 29% above median across all 562 scored occupations, and 11% below group median within Professionals occupations.