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1. Assessing the impact of technological change on similar occupations: Implications for employment alternatives

   https://doi.org/10.1371/journal.pone.0291428  

   Torosyan, Karine; Wang, Sicheng; Mack, Elizabeth A; Van_Fossen, Jenna A; Baker, Nathan (September 2023, PLOS ONE)

   Amavilah, Voxi Heinrich (Ed.)

   BackgroundThe fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. MethodWe first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. ResultsThe results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. ConclusionOur results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities. 

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2. AI exposure predicts unemployment risk: A new approach to technology-driven job loss

   https://doi.org/10.1093/pnasnexus/pgaf107  

   Frank, Morgan_R; Ahn, Yong-Yeol; Moro, Esteban; Kimbrough, ed., Erik (April 2025, PNAS Nexus)

   Abstract Is AI disrupting jobs and creating unemployment? This question has stirred public concern for job stability and motivated studies assessing occupations’ automation risk. These studies used readily available employment and wage statistics to quantify occupational changes for employed workers. However, they did not directly examine unemployment dynamics primarily due to the lack of data across occupations, geography, and time. Here, we overcome this barrier using monthly occupation-level unemployment data from each US state’s unemployment insurance office from 2010 to 2020 to assess AI exposure models, job separations, and unemployment through a new measure called unemployment risk. We demonstrate that standard employment statistics are inadequate proxies for occupations’ unemployment risk and find that individual AI exposure models are poor predictors of occupations’ unemployment risk states’ total unemployment rates, and states’ total job separation rates. However, an ensemble approach exhibits substantial predictive power, accounting for an additional 18% of variation in unemployment risk across occupations, states, and time compared to a baseline model that controls for education, occupations’ skills, seasonality, and regional effects. These results suggest that competing models may capture different aspects of AI exposure and that automation shapes US unemployment. Our results demonstrate the power of occupation-specific job disruption data and that efforts using only one AI exposure score will misrepresent AI’s impact on the future of work. 

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3. Identifying Alternative Occupations for Truck Drivers Displaced Due to Autonomous Vehicles by Leveraging the O*NET Database

   https://doi.org/10.1177/00027642221127239  

   Van Fossen, Jenna A.; Chang, Chu-Hsiang; Ford, J. Kevin; Mack, Elizabeth A.; R. Cotten, Shelia (September 2022, American Behavioral Scientist)

   Automation continues to be a disruptive force in the workforce. In particular, new automated technologies are projected to replace many mid-skill jobs, potentially displacing millions of workers. Career planning agencies and other organizations can help support workers if they are able to effectively identify optimal transition occupations for displaced workers. We drew upon the 24.2 Occupational Information Network (O*NET) Database to conduct two related studies that identify alternate occupations for truck drivers, who are at risk of job loss due to the adoption of autonomous vehicles. In Study 1, we statistically compared the jobs that we identified based on different search methods using O*NET classifications based on their similarity to the knowledge, skills, values, and interests held by truck drivers. In Study 2, we conducted a survey of truck drivers to evaluate their perceptions of the occupations identified as objectively similar to their occupation. Results indicate that optimal transition occupations may be identified by searching for occupations that share skills as well as the same work activities/industry as a given occupation. These findings hold further implications for career planning organizations and policymakers to ease workforce disruption due to automation. 

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4. Correlates of Native American engineering students’ career interests and efficacy: A test of SCCT

   Turner, Sherri L.; Mason-Chagil, Gale; Jacobs, Sue C.; Colston, Nicole; Johnson, Sarah; Patzkowsky, K. (August 2019, American Psychological Association 127th Annual Convention)

   There is an urgent need for young people to prepare for and pursue engineering careers. Engineering occupations comprise 20% of the science, technology, engineering, and math (STEM) jobs in the U.S. (Bureau of Labor Statistics, 2017). The average wage for STEM occupations is nearly double that of non-STEM occupations, with engineers commanding some of the highest salaries in STEM (Bureau of Labor Statistics, 2017). Moreover, engineering occupations are expected to be some of the fastest growing occupations in the U.S. over the next 10 years (Occupational Outlook Handbook, 2018); yet, there are current and projected shortages of workers in the engineering workforce so that many engineering jobs will go unfilled (Bureau of Labor Statistics, 2015) Native Americans are highly underrepresented in engineering (NSF, 2017). They comprise approximately 2% of the U.S. population (U.S. Census Bureau, 2013), but only 0.3% of engineers (Sandia National Laboratories, 2016). Thus, they are not positioned to attain a high-demand, high-growth, highly rewarding engineering job, nor to provide engineering expertise to meet the needs of their own communities or society at large. The purpose of this study was to examine factors that encourage or discourage Native American college students’ entry into engineering. Using Social Cognitive Career Theory (SCCT; Lent, Brown, & Hackett, 1994; 2000), we examined the correlates of these students’ interests and efficacy in engineering to accomplish this goal. Participants were N = 30 Native American engineering college students from the Midwest; 65% men, 30% women, and 4% other. The mean age was 25.87 (SD = 6.98). Data were collected over the period of one year on college campuses and at professional development conferences via an online survey hosted on Qualtrics. Three scales were used in the study: Mapping Vocational Challenges – Engineering (Lapan & Turner, 2000, 2016), the Perceptions of Barriers Scale (POB; McWhirter, 1998), and the Structured Career Development Inventory (Lapan & Turner, 2004). An a priori Power Analysis (f2 = .50; α = .05, 1 – β = .90) indicated our sample size was adequate. For all scales, full-scale Cronbach’s α reliabilities ranged from .82 to .86. Results of correlation analyses indicated that engineering efficacy was negatively related to lack of academic preparation (r = -.50, p = .016), and perceived lack of ability (r = -.53, p = .009), and positively related to academic achievement (r = .43, p = .043), career exploration (r = .47, p = .022), and approaching engineering studies proactively (r = .53, p = .009). Engineering interests were negatively related to perceived lack of ability (r = -.55, p = .007), and positively to proactivity (r = .42, p = .044), and academic achievement (r = .45, p = .033). Engineering interests were also related to support from parents, teachers, and friends to study engineering and pursue an engineering career. There was no significant relationship between engineering interests and engineering efficacy among these students. The relevance of these results will be discussed in light of SCCT, and recommendations for practice will be included. 

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5. Middle School Students’ Social Media Use

   Martin, Florence; Wang, Chuang; Petty, Teresa; Wang, Weichao; Wilkins, Patti (January 2018, Educational technology society)

   Cyber bullying, digital identity, impact of digital footprints, and use of inappropriate social media are topics that are gaining attention in K-12 schools. As more schools and school districts are implementing 1-1 and “bring your own technology” initiatives, attention to these topics is becoming increasingly important. A total of 593 middle school students were surveyed about digital footprints and concerns about social media. The results show that 17% started using social media at age nine or younger, 40% accepted friend requests from people they do not know, and 40% reported that their parents did not monitor their social media use, which calls for the needs of cyber-security education. These middle school students reported using social media most often to connect with their friends, share pictures, and find out what others are doing. They indicated that Instagram (27%), SnapChat (25%) and YouTube (25%) were their most used social media sites. These students have concerns about social media due to inappropriate postings, getting hacked, getting their feelings hurt, lack of privacy, inappropriate pictures, bullying, negativity, and stalkers. This study informs teachers, administrators, technology facilitators and parents on social media use by students. 

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