Reviewing the Exposure of Engineering Occupations to Artificial Intelligence
Bibliographic record
Abstract
An engineering degree should include learning the current skills required in a variety of jobs in an engineering discipline of choice. However, as new artificial intelligence (AI) technologies are implemented, the skills required by human workers will evolve. This paper builds on previous research which compared the verb/noun pairs in AI patents to those found in occupational descriptions to obtain numerical scores reflecting how exposed various occupations are to AI disruption. Improvements to this method, including a new selection method for identifying AI specific patents and the use of a different verb/noun extraction method, are used in this work. The updated method is then employed to identify tasks associated with engineering professions that are vulnerable to AI displacement. Specifically, it was found that Microsystems, Human Factors, and Electrical Engineering have relatively high exposure scores. The exposure of Electrical Engineering is further analyzed to determine the aspects of the occupation that are potentially most vulnerable to AI displacement. The analysis illustrates that the impact of AI might not undermine an entire task or skill. Rather, this type of emerging technology may displace or alter the nature of a task. This work is still in progress. The exposure scores themselves are not the focus, but they are a first step toward identifying the type of skills and engineering occupations that may be affected by AI technologies. This work has ramifications for engineering education as educators make choices about which skills to teach and which to abandon in the curriculum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".