Community colleges need to understand how artificial intelligence (AI) impacts them, and they also need to consider how to teach AI within the workforce programs offered to students and employers. Aside from Workforce Pell, no other workforce issue dominates community college workforce practitioners’ concerns as much as AI.
While there is little evidence on how fast companies are implementing these models, or what they do to companies’ skill needs, many community colleges have leaped ahead with programs to train AI technicians and jobs associated with AI. Much of the current knowledge of AI trends is based on broad, sweeping statements, often made by AI vendors or their supporters, that reveal little about firms’ actual activity at the local level. Moreover, because AI is used across all sectors of the American economy, it is often difficult to understand the specific impact within a sector and whether these mean different programs to prepare individuals to work.
However, it may help to learn from past experiences with new technologies and avoid repeating the same mistakes. As someone whose early career was connected with the first introductions of robotic technologies in American manufacturing over 40 years ago, here are some lessons worth considering.
Lessons learned
First, in the initial period of any new technology, there is always a tendency to overestimate the speed of implementation and the development of new occupations. When robotics were introduced, many colleges, including my own, quickly developed new robotic technician programs to give students the skills to program these devices. We were thrilled that enrollment in these programs soared and immediately congratulated ourselves on how leading-edge we were.
Of course, we did not understand not only how slowly manufacturers, particularly smaller firms, adopted this new technology, but also how hesitant they were to hire new people to work with robotic devices. Instead, they assigned engineers and other existing technical staff to robotics programs and did not easily open up new occupations. Our students, many of whom were autoworkers assuming they would keep their jobs by controlling the technology, were faced with little demand for the new skills they learned in our robotics technician programs. Our programs quickly lost popularity, and we learned that implementing newer technology in the private sector typically involves several stages before routinization.
I am certain this will be similar to AI implementation, so colleges need to understand the adoption process more specifically and not move too far ahead of the particular sector or firms in their community. An AI program may satisfy our desire to be “on the cutting edge,” but will it help our students get work?
Uncertain employers
Second, as firms developed sufficient experience with new robotic technologies, they were often unsure of how much to share that knowledge with their present workforce or hired additional workers. Often, they questioned whether we had the skill sets necessary to train their workers in these emerging fields. In many cases, manufacturing companies hired outside consultants and often other technology vendors on a contractual basis to care for the new robotic equipment. They both underestimated the ability of their current workforce and, more importantly, were not convinced that we in the community colleges were knowledgeable enough to train individuals.
For Macomb Community College and other community colleges with long, close ties to the auto industry, this meant employers had to be convinced that we had the technical competence. This often began with contract training, where we could provide maintenance training for robotic equipment. I suspect that colleges will likewise need to win employer support for AI programs through contract training or long-term technical partnerships with firms. We must demonstrate our competence to employers, not merely assert it in a few marketing brochures.
Third, to win employers’ trust in robotics and other forms of computer-based manufacturing technology, we needed important outside help. In the 1980s, the state of Michigan initiated a new private-public consulting organization called the Industrial Technology Institute. This organization provided technical support for understanding robotic technologies, but it also contributed a public policy perspective for community colleges as they developed their programs.
We also benefited from being able to hire people from industry close to retirement who were willing to help with program development. The need to develop a public policy perspective for AI programs will be even more important as this technology crosses over many different areas of technical programs.
However, with the exception of federal programs such the National Science Foundation’s Advanced Technological Education program, this response has yet to emerge at the federal or state levels. This is unfortunate because we will need a much clearer idea of what a public perspective on AI employment should be. In robotics, we could argue that increasing auto industry productivity would maintain the sector’s competitiveness and preserve high-wage jobs — a goal supported by our community and college leadership.
The public good of AI is still emerging, but colleges must help develop this perspective. Not only will they gain the trust of the community, but it may add an important dimension to the public debate of artificial intelligence, data centers and other aspects of this new technology.
Opportunities for everyone
Finally, community college workforce practitioners should focus on a dimension that, unfortunately, we did not recognize 40 years ago. This means embedding intentional efforts to provide opportunities for low-income working adults. Whatever employment impact AI will produce needs to open doors for individuals typically shut out by the traditional assumptions often made within the sector. Opening these doors for students is important work for community colleges. This is not only part of our public responsibility, but it also integrates discussions of AI into our institutions’ broader mission to reduce the growing economic inequalities in our society. Adding this dimension indicates we are a growing set of flexible institutions.
It also adds to the historical evolution of community college workforce development practice: Learning from the past to develop a more comprehensive perspective that can shape programs and community college responsiveness that will benefit our students and the communities we serve.
