Engineering Professors
Overall Exposure
2025 vs 2023
Theoretical Exposure
78What AI could do
Observed Exposure
40What AI actually does
Automation Risk Score
20Displacement risk
3-Year Outlook (2025 โ 2028)
Projected changes in AI automation metrics over the next 3 years based on estimated data.
Overall Exposure
2025 โ 2028 (estimated)
Theoretical Exposure
2025 โ 2028 (estimated)
Observed Exposure
2025 โ 2028 (estimated)
Automation Risk
2025 โ 2028 (estimated)
Exposure Metrics (2023 - 2028)
Detailed Metrics Table
| Year | Overall | Theoretical | Observed | Risk | Data Type |
|---|---|---|---|---|---|
| 2024 | 54 | 74 | 34 | 16 | actual |
| 2025 | 59 | 78 | 40 | 20 | estimated |
| 2026 | 64 | 82 | 46 | 24 | estimated |
| 2027 | 68 | 85 | 51 | 27 | estimated |
| 2028 | 72 | 88 | 56 | 30 | estimated |
Task Breakdown
About This Occupation
If you work as an Engineering Professor, AI is augmenting your curriculum development and research tasks. With an automation risk of 20/100 and overall exposure at 59%, this role faces high transformation. Lab exercise development sees 55% automation, while mentoring graduate students remains at 15%. BLS projects +8% growth through 2034.
Frequently Asked Questions
With an automation risk score of 20%, Engineering Professors has a low risk of AI replacement. Most tasks in this role require skills that are difficult for AI to replicate, such as complex decision-making, physical dexterity, or deep interpersonal interaction. AI is more likely to serve as a supportive tool.
The AI automation risk score for Engineering Professors is 20% (2025 data). Overall AI exposure is 59%, with 78% theoretical exposure and 40% observed exposure. The risk trend from 2023 to 2025 is 0 points.
The tasks with the highest automation potential for Engineering Professors are: Develop and update laboratory exercises and simulations (55%), Write grant proposals and manage research funding (52%), Mentor graduate students and supervise thesis research (15%). These rates reflect how much of each task current AI systems can handle, based on research data from Anthropic and academic sources.
The BLS projects +8% employment change for Engineering Professors from 2024 to 2034. Combined with an overall AI exposure of 59%, this occupation is experiencing both traditional labor market shifts and AI-driven transformation. Workers should monitor both employment trends and AI capability growth.
Since AI primarily augments capabilities in this role, professionals in Engineering Professors should embrace AI as a productivity multiplier. Focus on learning to use AI tools effectively, developing higher-order analytical and creative skills, and positioning yourself as someone who can leverage AI to deliver greater value.