Can Universities Prepare People for the Future of Work?
Alan Lesgold and Michael Bridges, August 14, 2023
The ideas are out there for the next generation of lifelong professional education. That generation will go beyond small-scale continuing education hours to allow professionals to respond to dynamic changes in the work roles that are produced by ever-more-powerful AI systems. Universities need to act now on these ideas. Providing the best lifelong professional learning cannot be done well by community colleges or employers alone. Universities that do act will be in much better shape than those that ignore, delay, or refuse to play.[1]
This essay presents some ways for universities to adapt to the new work dynamism. An interesting comparison is between the ideas in this essay and those put forward by ChatGPT and Supermind Ideator, which can be found here.
Introduction
Recent analyses of the emerging work world highlight the continuing shift from fixed jobs that are held for extended periods of time to more volatile work roles that shift and evolve in response to changes in organizational needs. This shift requires employers to continually monitor their ever-changing skill requirements and exercise best practices for filling those requirements more dynamically and efficiently. High performance organizations tend to have a combination of continuing employees who shift roles based on their skills along with gig workers and various outsourcing arrangements.[2] Good advice has been provided to enterprises regarding how to adapt to the continuing redistribution of tasks between humans and intelligent machines.[3]
That advice includes enough case studies to lend confidence to the view that work positions will continue to move from fixed, long-term job descriptions that constrain both what is asked of workers and what skill sets are available within an organization to a dynamic environment in which companies maintain strong inventories of the skills possessed by each of their employees as well as good intelligence about skills available from various external sources of workers, including freelancers, gig workers, and service agencies. And, of course, those inventories also include increasingly sophisticated understanding of what tasks are best handled by intelligent systems, including robots. As market demands change and as the available skill sets of an enterprise’s sources for work change, the most adaptive companies reallocate tasks to different people and machines. To keep the best people in the enterprise, they also adopt policies that make periodic shifts in task assignments a positive experience for their team members.
At the same time, professional workers often find themselves displaced because machines can do their jobs and they lack the skills that companies need in their human workers. Sometimes, companies will retrain them; sometimes workers will need to seek training externally. Private enterprise is moving rapidly to provide efficient and timely learning opportunities for skills in short supply and community colleges can do some of this, too. However, most universities are moving too slowly on focused, small-scale retraining possibilities, even when they are the optimal source for professional reskilling. They often lack a clear vision of where they should end up. In this essay, we describe a bit of the changing work environment, consider why universities are not yet adapting sufficiently to the changes, and propose some ways in which they could develop and implement clear goals that have internal buy-in.
The Nature of Work in the Information Age
One to two generations ago, jobs were stable, often lasting for years or even an entire career. Individuals who were ambitious or talented could learn new skills on the job and progress within an organization. In the past, possessing a college degree or a similar credential was often seen as the primary requirement for many professional jobs, especially entry-level positions. Because work was stable, college courses were good preparation for a lifetime of professional work. However, screening job applicants through degree requirements was implicitly discriminatory, as it disadvantaged those from less privileged backgrounds who lacked the resources, time, or opportunities to pursue college diplomas. Over time, the commitment to diversity and fairness in hiring led to a shift in hiring practices, focusing more on certified skills required for a job rather than a college degree. In addition, the rapid rise of generative AI has made job roles more dynamic, with workers regularly being displaced by AI systems or having to learn how to use such systems for aspects of their jobs. This has created a huge need for worker reskilling.
In this era of rapid AI emergence and increased concerns about diversity and equity, it is crucial to think in terms of learning opportunities and assessment processes for specific and dynamically changing skills. Every day, intelligent systems learn to perform new tasks or to assist humans in performing them more efficiently, displacing some workers and forcing others to learn how to interact with those systems. This has disrupted the stability of specific job tasks and has made the demand for different skills within organizations more volatile. Companies are adapting to this change in several ways, such as maintaining comprehensive databases of employees’ skills, outsourcing certain roles to agencies specialized in recruiting and assessing workers with specific talent, and delegating tasks or parts of tasks to contract workers and subcontractors.
Outsourcing arrangements enable economies of scale when multiple competing companies in the same area require a mix of specialized talents in varying quantities and at various times. For instance, a company named Unifi serves airlines in over 200 airports, providing services related to flights at those locations. When an airline adds a new flight at an airport, possibly their first at that site, they can contract with Unifi to handle ramp services, maintenance, plane cleaning, and ticketing instead of creating full-time positions that are only needed for a few hours a day. The “wing walker” who guides the pilot safely to the gate can assist one airline’s flight and then move a few gates down to marshal a plane from another airline, load baggage, refuel a plane, or push one from the gate. The Unifi team at any airport needs to adapt every time an aircraft type gets scheduled that has not previously been there. As AI systems improve, Unifi likely will switch to AI-driven kiosks to replace ticket handling, and its ticket handlers will need to find new skills for new jobs. This is just one example of how enterprises experience and address the need for rapidly changing skill requirements in the face of changing local demands.
In summary, the modern workplace, particularly in successful enterprises, is characterized by dynamic skill requirements, with some predictability of future needs but not complete certainty. This volatility creates new reskilling demand from displaced workers as well as enterprises with changing skill demands.
Dynamic Needs
Even if nothing else changes, the emergence of new AI tools will rapidly continue. Every day, there are reports of new start-up efforts to facilitate writing, coding, customer relations, information gathering, and other tasks currently done by humas. Indeed, a single site, gpte.ai, lists over 3,500 AI apps currently being offered. As new tools are adopted, the tasks a given worker carries out will change. Some tasks will be accomplished completely by intelligent entities. Some will be done partly by intelligent entities, and human workers will need to learn how to interact with those entities. The rapid emergence of new intelligent tools will create continual new work opportunities for workers. But, workers will need to become especially competent in filling the gaps that AI systems cannot or currently do not handle, often the hard stuff.
Companies will struggle to keep an ideal mix of human talent as they deploy intelligent systems, both to cut labor costs and to afford new productive opportunities. To make optimal use of their employees, employers will need skill inventories that show, in some detail, what skills, abilities, and experiences their employees possess. Then, as work needs change, current workers can be assigned new tasks that align with what they can do. Given the rapid takeover of work tasks by AI, the old notion of someone being hired to carry out a fixed set of tasks listed in a job description will not survive in the companies that thrive in this age of work dynamism.
Given the cost of recruiting new talent, companies will do best if they have the information needed to reallocate existing talent in their staff to meet new needs that might require many months to accommodate through new hiring. This will, of course, mean that companies also will do well to plan learning opportunities for their current staff to maximize their ability to cover new roles from within. The old notion that it is better to hire needed talent than to train existing staff is less likely to be true in the future, whether it was in the past.
While companies will thrive most when they maintain detailed data on the skill sets of their employees and plan in-house or outsourced training to evolve those skill sets efficiently, employees also will benefit from attending to the skills they have or can readily acquire, both prior to seeking new employment and while serving in existing work roles. In the past, a person prepared for work by completing a college degree curriculum to be generically qualified for a successful long-term work role or career path. Now the most successful people will keep an eye on the evolution of their careers and ask the hard questions about which skills will be afforded by learning experiences such as college, internship, or apprenticeship and then later about what additional skills they should acquire to maximize their future success, given the work role they occupy currently.
Imagine a multidimensional space in which each dimension represents a particular bundle of skill or understanding that might have value in the economy or civic life.[4] Every person might then be represented as a point in that multidimensional space, as might every current or predictable future work role. That point will reflect the extent to which the person has each of the skills that are represented in that space. Similarly, each job task in a company can also be represented as a point in that space that reflects which skills that task requires.
Each person will be asking what opportunities can become available based upon what new skills would need to be acquired. That amounts to asking which enterprise tasks are represented by points close to the one that represents that person. They will be tempted by new work roles that are close to their current location in the skill space and that provide best personal outcomes like salary, benefits, and job satisfaction.
Each company will be asking, for the total set of points in that space represented by the current workforce, which new kinds of task competence they now need or expect to need are attainable with minor enhancements of the current skill mix of their staff.
And, we argue, universities should be looking at that multidimensional space to see which skill clusters they should be focusing on as they attempt to keep curriculum aligned with the needs of civic society and the economy. They might also use such technology to scrape course syllabi for indications of which skills their current courses address.
Successful careers, successful enterprises, and successful educational entities will be those that find the most cost-effective ways to minimize the hysteresis between where they are and where they next need to go in that multidimensional space.
Multiple Sources of Skill
Further complications arise when there are multiple ways to acquire a skill. One person might learn a new skill completely on the job, without formal training. Another might acquire that skill by pursuing a certificate or degree at a university. A third might acquire the skill mostly through personal experimentation, with some online course elements to fill a few gaps. A fourth person might acquire the same skill through an internship or apprenticeship. Consider the skill of web design, for which all these paths are possible, though perhaps with slightly different resulting skill boundaries. The person who learns on their own at home might be less likely to develop a lot of capability for putting high-production-value video on a website, as might the person who has to do a website as a course requirement for a cognitive neuroscience course. Another person, learning on the job, might acquire extra capability using databases to drive web pages because it is needed in their job.
The bottom line: while a lot of a person’s labeled skill can be identical to that of others with the same labeled capability, there will be differences. This means that companies, workers, and universities need to understand that the same skill can be acquired in multiple ways and need to be prepared to specify skill elements at the edge of a skill’s definition when they matter. We return to this issue below by discussing steps universities and other actors might need to take. As the use of AI increases, personalized skill upgrading will become crucial to bridge between the detailed skill mixes of different employees and the emerging human roles related to current career tracks.
Universities Misfit the Needs of Productive Enterprises
Currently, universities are seeing an erosion of demand for what has been a profitable area, namely professional master’s degrees. Historically, people with professional master’s degrees could increase their workplace value — earn more money — and were often more likely to be able to pay substantial tuition fees compared to undergraduates. Universities have frequently depended upon professional master’s programming in their strategies for staying afloat financially. However, as the skill needs of workers become more dynamic, a full master’s degree program is both too long to complete, too likely to be partly obsolete by the time it is completed, and likely to be partly misaligned to a particular person’s career goals. Moreover, demands for reskilling often occur at times when a person’s finances are precarious, making the cost of reskilling a critical factor. Overall, university dependence on high-tuition master’s programing misfits the current expanding market for lifelong learning.
Built to Preserve Knowledge and Incremental Scholarship
Universities face some major challenges in adapting to the new realities of work task dynamism in the age of AI. For starters, universities were built to preserve more than to change. They began after the dark ages and were heavily influenced by the loss of scholarly knowledge during previous social disruptions. Even today, some universities are known for preserving a canon of “great books” that contain the accumulated wisdom of centuries. Such wisdom is important to the formation of strong intellect and social virtue, but it is not enough to prepare a person for a solid career in the modern world. Moreover, even durable wisdom can be acquired in many ways. Not only is it true that “wisdom can’t be told,”[5] but it also is the case that it can be acquired via multiple experiential paths.
Because universities were designed to preserve durable wisdom and are often filled with faculty who deeply embody that wisdom but tend to come from a limited experiential range, they tend to be very deliberate in changing what they teach and what they require for certifications. In a time of rapid change in which skills and knowledge are needed to have a productive life, an institution designed to preserve a fixed canon may not fare as well as entities that can adapt quickly. As a result, we can expect the private sector to capture much of the market for rapid reskilling while universities lose much of the advanced professional degree markets that are most profitable or require the least subsidy beyond tuition fees. This is true even though universities have been adapting slowly to the changes in knowledge requirements that decades of intense research and scholarship have produced.
Organized in the U.S. for the Benefit of Enterprising Scholars[6]
There are both good and bad aspects to the U.S. approach of locating much of the scholarly research base in universities. On the one hand, it has produced stronger research than anywhere else in the world. By recruiting top researchers as professors, universities ensure that the latest understanding of every discipline can (eventually) make its way into the curriculum of university programs. Moreover, at least in principle, there is some efficiency in having research and teaching share the same libraries, laboratories, and administrative infrastructure.
However, universities often must satisfy many demands to recruit top researchers. Among those demands is a frequent unwillingness of researchers to teach any more than they must. By using research grants to “buy out” of teaching and by refusing university job offers that require much teaching, top scholars too often are listed in university faculties while the courses most students take often are taught by adjunct instructors. As university finances become more difficult, those adjuncts are paid more poorly, because budgets must accommodate salary competition for top researchers, and high tuition levels are needed to accommodate the salary demands of researchers they may seldom see.
In addition to avoiding much teaching, many researchers also avoid involvement in setting curriculum for undergraduate and professional programming. They work on the doctoral curriculum because doctoral students will become their direct protégés but less so, in many cases, on curriculum for students not seeking to follow similar careers. As a result, curriculum tends to be set by other faculty who are less involved in major research but still may not be motivated by the need to adapt curriculum to emerging student needs, a task seen as less prestigious. This is especially the case for programming to meet learning needs that emerge for adults already in the work force.
Slow Moving
Beyond the just-discussed forces that push to keep curriculum from changing quickly, universities also have complex bureaucracies and decision protocols that contribute to their inertia. Universities’ preservationist ancestry and periodic political challenges push them to make decisions only after extended review of proposed changes by faculty bodies and by administrators trying to avoid financial, legal, or political damage. In our experience, it generally takes at least three years for a new academic program to be fielded, from initial conception to final launch, and it takes another year to do the needed student recruiting, development of individual courses, and other final steps.
As in any complex multiparty decision process, proposals can be killed or sent for rewriting at almost any stage. Sometimes, this is because serious flaws became known in later stages of deliberation, but not always. During author Lesgold’s half century of faculty and decanal life, he saw a major change in the undergraduate general education requirements of a university stalled and almost killed, even when all stakeholders thought the change was appropriate. The sticking point was that the proposed change would have altered the number of student credit hours attributed to one academic unit and thus eventually would have led to reallocation of positions from that unit to other units whose teaching would have increased under the proposed change. The same basic self-interest barrier prevented an undergraduate professional degree program from being put into place for about 20 years even though demand for it was substantial. On yet another occasion, he saw a proposed new program held up for six months because a faculty committee would not approve it until spelling errors in the proposal text were corrected and then would not give it time in their agenda for months after the typos were fixed. There also can be administrative delays in acquiring needed space, equipment, and software needed for new programs.
Now Facing Competition
In contrast, the private sector can act within months, as was the case for “courses” on using generative AI that were fielded within two months after ChatGPT became available and “how-to” blogs that appeared within a week after large language models became accessible. There is a good chance that the private sector will come to own much of professional re-education. Given that universities will face continuing shrinkage in their professional master’s programming, we suggest that they should enter the competition and try to preserve a strong presence, even in the face of competition with private entrepreneurs.
Universities Need Separate Units to Provide Professional Worker Reskilling
After a couple of generations of golden years for top university scholars and life tenure for those faculty who play the key role in program development, it is likely that many of them will resist major changes. They have led a good life where their teaching responsibilities were light and predictable and where their expertise within specific professions was unchallenged. Some will see the real threats ahead but will reason that they can hold out until they retire. In the current up-or-out tenure system, younger faculty will be too busy generating publishable research to take on the design of needed new learning opportunities. Any effort to push core faculty in a university to change will face fierce resistance that, as more faculties unionize, will be difficult to overcome. Moreover, universities would be less productive for our nation if top scholars were forced to divert attention to professional workforce reskilling, even though universities have much to offer in that area.
It seems worth considering a better path, creating substantial professional re-education units that operate as separate units within universities, reporting directly to the provost or president. Such units can hire instructional designers and educational technologists as needed and recruit instructors, sometimes from the university’s core faculty and sometimes from outside. This will still be seen internally as a threat, but as financial times get tougher, it should be possible to make such entrepreneurial efforts so long as the lives of faculty who prefer not to change that much are minimally impacted. Over time, as master’s programming continues to shrink and produce less revenue, professional schools within universities will work to develop relationships with such a new unit, which may be a better path than trying to get those schools to re-invent themselves immediately.
What Do Today’s Students Need?
We also should look at the situation from the viewpoint of students as we continue into the age of generative AI. A discussion of what should be included in initial liberal formation (undergraduate education)[7] and how professional re-education should be shaped requires a separate essay, which we expect to produce. However, a few key points need to be stated in this piece.
Undergraduates
Two trends have occurred over the decades in undergraduate education. First, there has been increasing demand that even undergraduate degrees be effective preparation for the work world. The press and government report the return on investment for bachelor’s degrees, comparing tuition costs with short-term earnings after graduation.[8] The second trend, which developed over a longer period, has been referred to as the shopping mall design (see Footnote 13) for curriculum, especially for the general education core (the parts of the degree requirement that deal with general formation as opposed to entry into a specific discipline). A university may require some coursework in the sciences, say, but offer many alternative choices. Similarly, it may require that some number of courses in a student’s program involve substantial writing but not specify what topics, disciplines, or genres that writing should involve. It is time to think hard about both these trends.
Certainly, it will be hard to convince potential students to spend dramatic amounts of time and money on a university degree when they can earn just as much without one. Yet, this is increasingly the case in many sectors. A variety of pressures, both related to corporate staffing effectiveness and to considerations of diversity and equity, are producing a substantial switch from job requirements stated in terms of required degrees to those stated in terms of verified skills. For example, upon becoming governor of the Commonwealth of Pennsylvania, Gov. Josh Shapiro issued an executive order calling for exactly this change in all state hiring.[9] All of this suggests strongly that universities need to learn how to assay their undergraduate course syllabi for content that relates to specific skills and how to reliably certify the attainment of those skills.[10]
In addition, the general education core of university undergraduate curricula should reflect the broad capabilities needed to fare well in the rapidly changing AI-enhanced world we are becoming. Traditional liberal formation assumed a world in which employment and work was stable and growth in a profession involved slow, incremental enhancement of skills on predictable career paths that were well worn. The world of work now involves rapid changes in needed skills within enterprises, including emergent needs for new skills not previously taught or learned. Today’s successful people need to have predictive knowledge of which roles are emerging close to their current skill set and what new learning will best prepare them for those emerging roles. In addition, they need financial skills and overall well-being to thrive, even if things are sometimes exceptionally challenging and finances sometimes fragile.[11] Ideally, undergraduate formation should include experience (real or simulated) in negotiating rapid changes in what students need to know, and the dynamic nature of the contexts in which they will need to apply what they know. They also need to learn how to tackle complex emergent problems, because AI can handle almost all routine problems and a lot more. Some of these competencies will be developed before college, but some will need to be included in the undergraduate curriculum.
Just-in-Time Opportunities for Lifelong Learners
The needs of post-baccalaureate learners are changing as well. Historically, universities have focused on professional master’s programming to provide specific training for career advancement. Today, that works less well, for several reasons.
- Students cannot or will not bear the financial costs.
- Students can spend only a limited amount of time in professional studies rather than financially productive work.
- The skills needed for work advancement, and sometimes just to retain employment, are changing too quickly to be acquired almost entirely prior to full professional engagement.
As noted above, the private sector has started to address the emerging market for short-term skill enhancement and certification of new skills, while professional master’s programming is moving from positive to negative status in the financial sustainability of universities. Both to better serve society and to remain financially strong, universities will need to address the short-term skill development market. Since that will require quite distinctive design of both learning opportunities and certification processes, it will be best accomplished through the creation of a separate central focus on such short-term offerings, as noted above.
How to Get from Here to There
Universities that wish to remain central to society in the information age will need to make many changes, and entrenched interests will resist most of them. We are optimistic that at least some universities will find their way to a new wave of importance, public appreciation, and governmental support. However, some will fail, just as many businesses have failed to survive past revolutions in productive human work and were replaced by new enterprises that did not face internal social inertia. Inevitably, many of the new enterprises serving the professional reskilling market will be private businesses, which will further erode the financial base for great universities that cannot adapt.
So, what needs to be done? There are two major tasks. First, the role of the university in initial formation and preparation for adult productive life — the undergraduate core — needs to be redesigned and more clearly motivated by emerging as well as enduring societal and individual student needs. Second, some of the professional development functions previously addressed with professional master’s programs[12] need to be redesigned to support lifelong learning and continual needs for reskilling as intelligent systems replace aspects of human professional work.
Reinvigorate the General Education Core
As previously discussed, historically, a key role of undergraduate curriculum was to provide broad understanding and immersion in the wisdom that had accumulated over the centuries. Knowledge changed slowly, so understanding history, philosophy, math, and science (and sometimes religion) was what distinguished the graduate of a great university from those not fortunate enough to have university formation. In recent years, we have realized that the liberal core of the great universities did not adequately capture the accumulated wisdom of non-white, non-Western cultures and often slighted wisdom developed by women. Efforts have been made to expand the general education core to take account of both these shortfalls and of the failure of the transmitted core to capture the ways in which the dominant male, white, Western corpus of literature ignored the mistreatment as well as the wisdom of women and non-white and Aboriginal cultures.
As noted above, while the liberal core of the undergraduate curriculum originally was monolithic, a combination of disagreements over what was important to include, considerations of resource distribution tied to curriculum, and contested efforts to diversify the cultures treated by the core led to fragmentation. Curriculum was changed from a common core to a “shopping mall” in which students can choose which wisdom they want to acquire.[13] It is time for universities to resume their historic role in directing students to the learning opportunities most likely to prepare them to be good citizens, productive working contributors to society, and able to thrive and lead good lives in the information age.
This will require robust design work by the wisest faculty in the university. The design team should include philosophers, historians, scholars of literature, social scientists, STEM experts, business researchers, and information and computing scientists. Several recent books and articles address some of the needs (see Footnote 11 reference to Lesgold’s 2019 book and the Fox reference in Footnote 13), but each university can and should decide its own path. One role of senior university leadership is to ensure that decisions of such a study group are not influenced by parochial financial and power issues. Leaders need to ensure that self-interest is not prioritized over the needs of students and that such selfishness is not rewarded. Rather, leaders need to inspire and help those designing a new general education curriculum to rise above territorial self-interest.
Build Strong Badging Systems
While the general trend among companies searching for people to fill jobs is to list required skills rather than degrees, there remains one sticking point. When one has a degree, there is a lot of information available about what completing that degree required, and there are accrediting agencies that review whether a specific degree requires documented knowledge and skills. When offering smaller learning opportunities such as learning a specific skill, there is less clarity about what “passing” a particular short course means. For this reason, it is essential that universities develop arrangements that make it easy to verify microcredentials (badges). This has two parts: validating possession of the credential by a particular person and providing information about what skill was acquired and how its acquisition was measured. Several blockchain-based microcredential services exist,[14] and universities need to use one that is widely used by employers and that provides validation of a user’s possession of the credential. In addition, the record of a person’s skill certification should include a link to information about how that certification was determined (e.g., through demonstration in a simulated work task, through employer validation of use of the skill on the job, through written examination, etc.). It is likely that accrediting bodies will address the issue of skill certification over time. When that happens, a “badge” can be recorded simply as meeting the requirements of a named accrediting body.
The bottom line is that when universities provide learning opportunities for professional workers needing new skills, robust systems should exist to document completion of specific learning opportunities, what those opportunities afforded the learner, and how achievement was certified.
Move Toward Lifelong Subscriptions
University learning opportunities are expensive to offer, and reskilling opportunities are most needed when financial well-being is under threat. Moreover, the cost for a specific retraining opportunity will seem quite high to potential students regardless of circumstances. This suggests that the current model of price-per-credit needs to be changed. One possible new model is a subscription arrangement.
In such an arrangement, professionals would subscribe to a university’s professional skill upgrading program, paying a fixed amount monthly or annually. Subscription would entitle the student to some amount of learning opportunity. This could be in the form of entitlement to a fixed number of days of learning, access to a library of online modules, or access to a fixed number of courses during the subscription term. Subscribers might get offerings at no added cost or might get a steep discount on each offering (the latter would help ensure that students take each course seriously and don’t simply sign up for a quick peek but not really engage in learning). There are many possibilities, and it might be wise for universities to start experimenting with them. Like the private sector, they might start with small free offerings of online modules (with a charge for certification) and then invite selected subscribers for a beta version of a subscription program, expanding to a final arrangement as specific options prove themselves.
This would be a novel way for universities to do business. But, it is what the private sector often does quite successfully. If universities are to compete, they must consider whether such experimentation is more likely to be successful than sticking to existing models.
Develop New Partnerships
Just as universities learned to work with private enterprises to bring new technologies to society, they also will benefit from forming partnerships for worker reskilling. Three important elements are worth pursuing.
First, in a world where reskilling is largely aimed at helping workers fill needs of organizations that might employ them, it is essential for universities to learn from employers. While universities will sometimes be aware of learning needs that employers miss, employers know what skills are hard for them to find, and those are the ones that they will seek in new hires. Both to maximize the value of learning opportunities they offer and to make those offerings more attractive to potential students, universities will need to form strong partnerships with companies and other employers that are truly two-way, with each party respecting and learning from the other.
Second, professional reskilling units will need a lot of information about which elements of new learning opportunities work well for which learners. In addition to advisory panels, surveys, and focus groups, units should learn from the app development world and provide beta versions of new learning opportunities free for a limited time in order to get detailed information about what works and what needs to be improved. Such arrangements also produce helpful word-of-mouth and social media “advertising,” as the beta users recommend the refined learning opportunities to other workers and employers.
Third, such partnerships may also lead to a different marketing situation for universities, in which their client is not the individual student alone but rather an organization that wants to provide a university-developed opportunity to their employees or clients. A company may want a training module to help their employees prepare for emerging roles. A nonprofit or government entity might want a module to help their clients become work-ready. Such “corporate” clients might be more able to provide payment sufficient to make a professional reskilling unit revenue neutral or even positive than individual student tuition payments can.
Diversity, Equity, and Inclusion Are More Important than Ever
So far, universities have focused on assuring more equitable access to existing curriculum. Generally, this involves a combination of more personalized admission decisions, enhanced scholarship availability for those from less wealthy backgrounds, and pre-college preparatory programming to help assure success in current degree programs. All of that is important and should continue. However, when developing programming for adult reskilling, more will be required.
First, since the purpose of such lifelong learning opportunities is to help adults “retread” to prepare for new jobs, it will be important to see learning opportunities as paths from where the learners currently are to where they need to be to enter new work roles. It is possible that for some emerging skill areas, the starting points for adults from different economic and/or ethnic groups will differ. Learning modules for lifelong learning need to be designed to fairly allow all students to move from where they are to where the learning opportunity is aiming. At least, there should be equal availability of learning opportunities of similar value — in terms of status and earnings — to students with different starting points. This will be challenging, but the only way to progress toward such fairness is to have data gathering and evaluation schemes designed not only to certify skill but also to reveal any need for more flexible or tailored opportunities to acquire skill.
Create a Dedicated Professional Re-Education Unit for Programming Agility
In summary, we believe that a critical action step for a university seeking to develop substantial professional reskilling opportunities is to establish a separate unit for this work. The top players in the space have tended to do this (e.g., Harvard, Arizona State, Purdue), while some have partnered with an online offeror instead (e.g., Georgia Tech). The advantage of a separate unit is that it allows for a start with minimal inertia. The unit can serve as a central point-of-contact in establishing, facilitating, and managing interactions, collaborations, and partnerships with employers, business development organizations, and other stakeholders. It can monitor and track national, regional, and local market trends in labor projections, workforce needs, and reskilling activities. It can establish processes (e.g., surveys, focus groups, etc.) to collect valuable information from students, employers, and workforce/upskilling organizations. It also can comb syllabi in professional programs of a university’s schools/colleges to build a list of course elements that address potential reskilling needs. And, it can recruit instructional designers/engineers who can build efficient learning opportunities and assessment schemes and field them quickly. As an entity outside the credit course world in the university, it also may have more pricing freedom, both to use freebies to attract beta participants and build a market and to price offerings competitively rather than within a rigid price-per-credit model.
Most important, a dedicated unit can be relatively free of the limiting organizational inertia that keeps universities from competing effectively to provide extensive reskilling opportunities. It is easy to see and measure how quickly start-ups and other entrepreneurs field new offerings. Universities need to find ways to catch up with that speed without incurring the wrath of disciplinary units or damaging them. An independent unit is one way to do this.
Footnotes
[1] Graphics created using playgroundai.com.
[2] Jesuthasan, R., & Boudreau, J. (2021). Work without jobs. MIT Sloan Management Review. Jesuthasan, R., & Boudreau, J. W. (2022). Work without jobs: How to reboot your organization’s work operating system. MIT Press.
[3] See Footnote 2.
[4] The following paragraphs are likely related to work on skill analysis by Skyhive. See https://patents.google.com/patent/US11164153B1/en?assignee=Skyhive&oq=Skyhive for one of their patents related to what they call Quantum Labor Analysis. See also https://news.law/creator-of-quantum-labor-analysis-r-skyhive-granted-u-s-patent-for-job-description-generator/.
[5] Gragg, C. (1951). Because Wisdom Can’t Be Told. In K. Andrews (Ed.), The Case Method of Teaching Human Relations and Administration: An Interim Statement (pp. 3–12). Cambridge, MA and London, England: Harvard University Press. https://doi.org/10.4159/harvard.9780674594500.c3
[6] Some readers will argue that many researchers in universities are much more idealistic and dedicated to teaching than we discuss. That is true. However, the discussion above remains true of enough of a research university faculty base that the impacts described are real and substantial. As a dean, one of us encountered demands for minimal teaching from every hire of a researcher to his faculty.
[7] For a preliminary view of how curriculum should address the age of AI, see Lesgold, A. M. (2019). Learning for the age of artificial intelligence: Eight education competences. Routledge.
[8] See, for example, https://www.usnews.com/education/best-colleges/articles/college-majors-with-the-best-return-on-investment. See also Lesgold, A. (2022). Toward a new hidden curriculum. Medium. https://medium.com/about-work/toward-a-new-hidden-curriculum-f49572eef2e1.
[9] https://www.penncapital-star.com/government-politics/in-his-first-executive-order-shapiro-removes-degree-requirement-for-thousands-of-state-jobs/
[10] See Footnote 4.
[11] Lesgold addressed some of this in his book a few years ago. See Footnote 8.
[12] Some universities are finding it productive to make short-term programming “stackable.” That is, students can present their certifications in a collection of such offerings for credit toward a graduate degree.
[13] While it refers to high school, a relevant reference is Powell, A. G., Farrer, E., & Cohen, D.K. (1985). The Shopping Mall High School. Winners and Losers in the Educational Marketplace. NASSP Bulletin, 69(483), 40–51. See also Fox, C. R. (2016). A liberal education for the 21st century: Some reflections on general education. Currents in Teaching & Learning, 8(2), 5–17.
[14] Examples include Accredible, Blockcerts, Doxychain, BadgeCert, BCDiploma, and Opencerts.
