This Work-in-Progress (WIP) paper investigates how artificial intelligence (AI) may reshape engineering work through a task-based analysis of nationally standardized occupational descriptions. Rather than forecasting job displacement or evaluating specific AI tools, this study examines how AI may interact with the tasks that define engineering practice across disciplines. Guided by human capital complementarity theory, we analyzed task descriptions from the U.S. Department of Labor’s O*NET database for engineering occupations. Using qualitative content analysis, each task was coded according to its likely relationship with AI as substitutable, augmentable, complementary, or emergent. Preliminary findings indicate that AI is more often positioned as augmenting or complementing engineering work than replacing it. Augmentable tasks were most common in analytical and modeling-intensive activities, where engineers remained responsible for interpreting outputs and validating results. Complementary tasks were prominent in integrative work, emphasizing human judgment, coordination, and accountability. In contrast, substitutable and emergent tasks were less common. These findings suggest that the enduring value of engineers’ work lies not only in technical execution, but in interpretation, judgment, and responsibility. As such, engineering education should increasingly prepare students to critically engage AI-generated outputs, integrate technical and contextual considerations, and exercise professional judgment in complex practice.
http://orcid.org/0000-0002-9929-5587
Purdue University – West Lafayette (College of Engineering)
[biography]
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