AI in Education: Addressing Obstacles to Open Opportunities

Pam Sornson, JD

Pam Sornson, JD

April 16, 2024

Perhaps the most prominent concern raised by the use of Artificial Intelligence (AI) and Generative Artificial Intelligence (GAI) is the potential threat of ‘technological unemployment.’ The issue stems from the very real possibility that emerging digital resources have the capacity to eliminate jobs currently held by humans. The work would be transferred to robots, automated machines that are capable of performing the activities faster, more efficiently, and more profitably. Both employers and industries are interested in exploring any labor-related option that promises to increase productivity without also unnecessarily increasing expenses. That reality is driving the current uptick in AI technology investments, especially in the healthcare, finance, energy, and agriculture industries. Workers in almost all sectors are rightfully concerned that their occupation might be one that becomes obsolete due to the adoption of technological tools.

However, many economic experts do not believe that GAI will cause widespread job layoffs and high, sustained unemployment. Instead, they posit that the newly embraced digital agency will trigger the development and growth of as-yet unidentified occupations and industries, which has been the trajectory of the global workforce through each of the past three ‘industrial revolutions.’ Their premise is that the future looks different – not worse – because of GAI and that the community has the tools in place to ensure that the changes triggered by GAI bring positive economic development, not certain financial ruin for millions of workers.

 

AI as a Workforce Disruptor

The phrase ‘technological unemployment’ certainly triggers fears in today’s technically fraught digital society, even though it isn’t a recent statement. Economist John Maynard Keynes first used it in 1933 while lamenting the fact that ‘economizing the use of labor’ (through the adoption of steam and mechanically produced power sources) had reduced the need for human effort without, at the same time, providing an alternative occupation for the displaced workers. His concern remains valid, as workers in all industries – and at all levels of industry – assess whether their workplace activities are susceptible to digital automation. At this stage of the ‘4th Industrial Revolution,’ it seems like no job or position is off limits to a potential AI intervention.

One industry professional is actively researching which industries or sectors are most susceptible to experiencing technological unemployment based on the types of jobs they typically offer. Cameron Sublett, the director of the Educational Leadership & Policy Studies Department at the University of Tennessee, Knoxville, has been researching the potential impact of AI on education, particularly Career & Technical Education (CTE).

His analysis indicates that occupations that involve research, thinking, and analysis are not good candidates for an AI-driven replacement. These jobs require higher levels of education (bachelor’s degrees and beyond) and the mastery of transferable skills like critical thinking, creativity, etc. The skills are transferable because they can be deployed in many occupations and settings.

Alternatively, sectors most vulnerable to an AI takeover are those that demand technical skills, which require specialized knowledge of specific machines or processes. In many cases, technical skills are easily replaceable by AI and machines because they command little or no ‘thinking’ to complete.

Sublett then applied his assessment to the 16 Career Clusters outlined in the National Career Clusters® Framework to determine which careers (and therefore which training programs) will be most impacted by the adoption of the emerging ‘AI workforce.’ Reviewing that data through an educational achievement lens (high school diploma, associate degree, and bachelor’s degree), he identified which career clusters are more likely to see AI interventions sooner rather than later:

Those clusters with high numbers of high school graduates as workers are the most vulnerable to technological unemployment. Their jobs are typically rote and mechanical, making them easier to automate. These industries include architecture and construction, hospitality and tourism, manufacturing, and transportation.

Those clusters most reliant on transferable skills are least likely to see an influx of machines because they rely on human creativity and ingenuity to thrive. These industries include business management, health services, human services, and legal services.

Across all clusters, higher levels of education apparently act as barriers (or at least hurdles) to the automation threat, at least for now.

Based on his data, Sublett suggests that today’s CTE courses and programs can do a better job of integrating transferable skills into technical training. He posits that well-trained CTE graduates should be better prepared for automation than their BA or MA counterparts because their certification programs can more easily integrate the two skill sets into current training programs. Recently expanded funding for CTE programs can help move that integration process forward.

 

AI as an Education Enhancer

Sublett isn’t alone in his assessment that current CTE training programs can be modified to embrace the power and opportunities of AI and GAI. The World Economic Forum (WEF) is also advocating for changes in traditional technical educational programs to embrace AI and all its opportunities.

The WEF sees promise in AI’s capacity to not just help learners learn but also to help teachers teach. The organization notes that finding a solution is not the only aspect of a complete project; clearly and accurately defining the problem is also a critical element of project success. It supports a new learning method, the PAIR Framework (problem, AI, interaction, reflection) that guides users through the AI assessment process by assisting them to:

formulate the problem,

explore and find the most relevant AI tools,

interact with both AI tools and relevant facts, and

reflect on the process, their conclusions, and their experiences with the technology.

The framework provides a strategy for familiarizing users with AI’s capabilities and then assessing its effectiveness before determining whether the digital assistance was beneficial to their project. The framework also gives new AI users the skills needed to develop and utilize transferable skills, a skill set itself that can be applied to any training program or occupation.

For teachers, AI also expands the capacity to reach every student with lessons and plans personalized for the individual. Using an AI resource, the teacher can modify course materials to match the needs of the specific student and then use it again to assess how well the learner is mastering the work. As a teaching tool, AI has the capacity to effectively respond to teacher shortages and educator burnout by vastly expanding the reach and scope of every class and program to meet the needs of all learners.

 

Despite the potential it poses to upend much of the world’s workforce, emerging research suggests that AI might instead provide more people with better skills and more opportunities to improve their economic situation. Schools that add AI training to their existing programs will expand their students’ capacities beyond the risk of technological unemployment.

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