Share and intensity of work current AI systems can materially affect.
SOC 25-1124
Foreign Language and Literature Teachers, Postsecondary AI displacement risk
Foreign language faculty teach speaking, writing, and literature while machine translation handles casual fluency. That pressures enrollment in basic sequences — the honest headwind — while shifting the discipline's value toward cultural expertise, literature, and the advanced proficiency machines fumble.
Likely potential for exposed tasks to move to software after workflow integration.
Distinct from working translators: this role teaches rather than practices. Language-app competition is real for introductory courses; the durable enrollment is in majors, heritage speakers, and study-abroad programs where the subject is culture, not vocabulary.
Distribution
Where Foreign Language and Literature Teachers, Postsecondary sits across 620 tracked roles
Displacement pressure 36 — higher than 61% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
24 O*NET task statements matched to SOC 25-1124. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $79,350 (May 2025, US national). The latest BLS row matched SOC 25-1124.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Foreign Language and Literature Teachers, Postsecondary
SOC 25-1124 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 36/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Foreign Language and Literature Teachers, Postsecondary
The current evidence import matched 24 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks24
SOC25-1124
- Core task / ID 6335
Prepare course materials, such as syllabi, homework assignments, and handouts.
- Core task / ID 6337
Maintain student attendance records, grades, and other required records.
- Core task / ID 6334
Evaluate and grade students' class work, assignments, and papers.
- Core task / ID 6336
Initiate, facilitate, and moderate classroom discussions.
- Core task / ID 6340
Prepare and deliver lectures to undergraduate or graduate students on topics such as how to speak and write a foreign language and the cultural aspects of areas where a particular language is used.
- Core task / ID 6345
Conduct research in a particular field of knowledge and publish findings in scholarly journals, books, or electronic media.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Prepare and deliver language and culture lectures
Exposure 56, automation 28%, augmentation 70%.
O*NET evidence: Prepare and deliver lectures to undergraduate or graduate students on topics such as ho... (ID 6340)
Evaluate and grade student work
Exposure 54, automation 30%, augmentation 62%.
O*NET evidence: Evaluate and grade students' class work, assignments, and papers. (ID 6334)
Initiate and moderate classroom discussions
Exposure 30, automation 10%, augmentation 56%.
O*NET evidence: Initiate, facilitate, and moderate classroom discussions. (ID 6336)
Conduct research and publish findings
Exposure 50, automation 25%, augmentation 66%.
O*NET evidence: Conduct research in a particular field of knowledge and publish findings in scholarly j... (ID 6345)
TaskExposureAutomationAugmentation
Prepare and deliver language and culture lectures5628%70%
Evaluate and grade student work5430%62%
Initiate and moderate classroom discussions3010%56%
Conduct research and publish findings5025%66%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
ModerateDepartment Chair
Training horizon: 12-24 months. Skill overlap 64. Wage preservation signal 116.
- Lead program redesign
- Grow heritage-speaker tracks
- Own program outcomes
role redesign
ModerateLanguage Program Director
Training horizon: 6-12 months. Skill overlap 68. Wage preservation signal 108.
- Run intensive programs
- Manage placement testing
- Lead study-abroad partnerships
Comparison guides
Compare the next move before you commit
12-24 months
Foreign Language and Literature Teachers, Postsecondary to Department Chair
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Foreign Language and Literature Teachers, Postsecondary into Department Chair.
6-12 months
Foreign Language and Literature Teachers, Postsecondary to Language Program Director
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Foreign Language and Literature Teachers, Postsecondary into Language Program Director.
What the AI risk score means for Foreign Language and Literature Teachers, Postsecondary
The displacement pressure score for Foreign Language and Literature Teachers, Postsecondary is 36. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Evaluate and grade student work carries 30% automation pressure, while Prepare and deliver language and culture lectures carries 70% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $79,350 (May 2025, US national). Employment context: Faculty role facing the machine-translation question directly. Typical education: Doctoral degree typical.
Wage vulnerability is 32, while transition feasibility is 68. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Apps absorb introductory enrollment
- Cultural and literary teaching persists
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Foreign Language and Literature Teachers, Postsecondary, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Language instruction
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Cultural expertise
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Curriculum design
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Academic writing
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Department Chair, such as lead program redesign.
- By 90 days, compare internal openings and external postings for Department Chair or Language Program Director and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Foreign Language and Literature Teachers, Postsecondary
Will AI replace Foreign Language and Literature Teachers, Postsecondary?
Foreign language faculty teach speaking, writing, and literature while machine translation handles casual fluency. That pressures enrollment in basic sequences — the honest headwind — while shifting the discipline's value toward cultural expertise, literature, and the advanced proficiency machines fumble. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Foreign Language and Literature Teachers, Postsecondary work are most exposed to AI?
Evaluate and grade student work and Prepare and deliver language and culture lectures show the strongest automation pressure in this model. Prepare and deliver language and culture lectures and Conduct research and publish findings are better treated as AI-augmented work.
What should Foreign Language and Literature Teachers, Postsecondary learn next?
Start with Language instruction, Cultural expertise, Curriculum design. The most practical adjacent paths in this model are Department Chair and Language Program Director.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics