An AI-assisted glimpse into the present and Future of Technical Communication: Description and Prediction from Real-time Labor Market Analytics
The socio-technological landscape of work has been radically and permanently changed by the ever-increasing commercial use of artificial intelligence (AI) tools. Predicted to put 30-47% of jobs in the U.S. and about 70% of jobs in China at high risk of being replaced, AI technologies enjoy many advantages over human labor – large scalability, computation capacity, and strong ability to detect trends (Frey & Osborne, 2013; Bostrom, 2014; ). The ongoing COVID-19 outbreak only exacerbates such disruption with accelerating automation, increasing unemployment rates, involution, and business shutdown. Artificial intelligence (AI) has profoundly changed how labor market information can be generated at the macro-level to help shape educational programs and workforce policies. Drawing on academic and trade literature, government publications, white papers, and technical reports, this talk introduces the Occupational Information Network (O*NET), a traditional labor market information tool sponsored by the U.S. Department of Labor, as well as two RT LMI service providers, Economic Modeling (Emsi) and Burning Glass before exploring ethical concerns and implications surrounding the use of RT LMI. It explores how state-of-the-art tools use AI to examine labor market trends and how such findings are presented to customers as paid or free service. The presenter will focus on analytics provided by RT LMI technologies about technical communication as a field to answer a few critical descriptive and predictive questions about technical communication in the U.S.: What skills are needed in the industry? What qualifications are required by the field? How are candidates with different credentials compensated? How much growth can technical communication have in the next ten years? How likely will technical communication work be automated? It will end with a brief discussion about possible ways for universities and researchers to collaboratively build their own limited-scale labor market analytics to help provide research-informed insights.