Teaching Assistants, Postsecondary
The routine layer of this job is compressing.
A substantial share of this occupation's activities overlaps with what AI already does well. Expect the routine layer to compress first, and the human-anchored layer to gain weight and value.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Assist faculty or other instructional staff in postsecondary institutions by performing instructional support activities, such as developing teaching materials, leading discussion groups, preparing and giving examinations, and grading examinations or papers.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Assess student capabilities, needs, or performance. | 18.3% | Not observed 0.00 |
| Teach academic or vocational subjects. | 14.9% | High overlap 0.62 |
| Distribute materials, supplies, or resources. | 12.9% | Not observed 0.00 |
| Communicate with others about operational plans or activities. | 9.5% | High overlap 0.61 |
| Prepare informational or instructional materials. | 8.8% | High overlap 0.73 |
| Assist scientists, scholars, or technical specialists with projects or research. | 7.7% | Moderate 0.56 |
| Supervise personnel activities. | 7.5% | Not observed 0.00 |
| Schedule operational activities. | 5.9% | Not observed 0.00 |
| Purchase goods or services. | 4.0% | Moderate 0.52 |
| Maintain current knowledge in area of expertise. | 3.7% | High overlap 0.61 |
| Monitor individual behavior or performance. | 3.4% | Moderate 0.45 |
| Train others to use equipment or products. | 3.4% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is teach academic or vocational subjects; the most important activity AI is not observed doing is assess student capabilities, needs, or performance. The plan below is built around exactly that split.
Move first: compress the routine before it compresses you.
Next 30 days
- Take the exposed layer yourself. Your most AI-overlapped activity, Teach academic or vocational subjects, is exactly what a well-tooled colleague will accelerate. Be that colleague first.
- Time-box the routine. Measure how long your repeatable tasks actually take; that's the budget AI adoption will compete against, and the time you will reinvest.
Next 90 days
- Double down where humans hold. Grow the share of your week spent on work like Assess student capabilities, needs, or performance, measured as outside AI's current reach, and take on more of it explicitly.
- Build one AI-assisted workflow end-to-end for your team and own its quality. Being the local standard-setter compounds.
The longer game
- Position at the boundary. The valuable seat in partially-exposed occupations is the translator: someone fluent in both the domain and the tools. Certifications matter less than a demonstrated workflow portfolio.
- Recheck yearly. Elevated-band occupations move: new tools shift specific activities fast. The data vintage is printed at the top of this page.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.