Methods for Evaluating Engineering Occupational Exposure to Artificial Intelligence

As artificial intelligence (AI) technologies advance rapidly, their impact on the engineering profession is potentially imminent. This paper provides a systematic review and comprehensive comparison of existing methods to assess occupational exposure to AI applications. By evaluating 36 papers and reports, we categorize the methods into five themes: expert opinions, qualitative surveys, quantitative approaches, deep learning techniques including Natural Language Processing (NLP), and analyses utilizing previous models. The strengths and weaknesses of each method are examined based on key metrics, including analysis time, strength of data sources, reliability, and specificity at a task level. This review offers insights into the strengths of current approaches and provides recommendations for addressing limitations in assessing AI's potential disruption of the workforce.