Engineering Teachers, Postsecondary

Teach courses pertaining to the application of physical laws and principles of engineering for the development of machines, materials, instruments, processes, and services. Includes teachers of subjects such as chemical, civil, electrical, industrial, mechanical, mineral, and petroleum engineering. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.

The occupation "Engineering Teachers, Postsecondary" has an automation risk of 40.9%, which is closely aligned with a calculated base risk of 41.7%. This moderate risk level suggests that while many tasks performed by engineering professors can be supported or partially replaced by technology, their roles continue to rely heavily on distinctively human abilities. Automation technologies have proven proficient at handling structured, repetitive, or data-driven aspects of instruction and research, but face challenges with more nuanced aspects that require human judgment, creativity, and interaction. As such, the risk level reflects a balance between tasks susceptible to automation and those that are highly resistant. The tasks most vulnerable to automation include supervising student research and internships, writing grant proposals, and conducting research for publication. Supervising academic work, especially at the undergraduate level, often follows standardized procedures and assessment criteria, making it amenable to digital platforms and AI-driven oversight. Similarly, the process of drafting grant proposals and conducting research leverages information synthesis and data analysis—areas where natural language processing and knowledge engines excel. However, while automation can assist with literature reviews, data collection, and even drafting routine proposal sections, it is less adept at the novel idea generation and complex synthesis required for ground-breaking research. On the other hand, the occupation is buffered by tasks that demand deep subject expertise and interpersonal engagement, such as compiling specialized bibliographies for students, offering professional consulting services to industry or government, and advising student organizations. These tasks resist automation due to their reliance on contextual understanding, mentorship, and the ability to navigate unique and evolving challenges. Bottleneck skills—especially originality, which is cited at both 3.3% and 3.9% resistance—serve as critical barriers, as they reflect the need for unique, creative thought in both teaching and research guidance. Consequently, while engineering professors may see elements of their jobs enhanced or streamlined by AI, the core functions rooted in creativity, mentorship, and professional judgment will continue to safeguard the profession from full automation.