The Smart Machines Around Us

These increasingly capable systems not only retrieve and present information more quickly and accurately but also solve problems and offer advice. Machine learning allows computers to "consume" information such as medical records, financial data, purchases, an...

Augmenting Human Expertise

AI and robotics have catalyzed a wave of automation—based on artificial cognition, cheap sensors, machine learning, and distributed smarts—that will touch virtually all jobs, from manual labor to knowledge work. However, automation may be a less apt term than augmentation. As Garry Kasparov, former world chess champion, has observed: "Humans are not being replaced by AI, we are being promoted. Machine-generated insights add to ours, extending our intelligence in the way a telescope extends our vision. Think of AI as 'augmented intelligence.' Our increasingly intelligent machines are making us smarter."6As machines can do more, professional roles shift. New tasks take the place of the ones that were automated. Historically, new technologies have spurred the creation of more jobs than they have destroyed. For example, in the United Kingdom, automation is estimated to have eliminated 800,000 lower-skilled jobs (e.g., call centers) while simultaneously creating 3.5 million higher-skilled ones. The higher-skilled positions often require retraining, however. At German auto-parts maker Bosch, welders, joiners, and mechanics were trained in basic coding skills to enable them to use robots as tools.7Thus, whether it is AI, robotics, or another technology, today's machines can work alongside professionals as partners, amplifying human performance and augmenting human intelligence.

Data-Driven Insights

"Knowledge processing"—something much more sophisticated than information retrieval—is an example of a new approach to professional work. Today's systems can capture and reuse massive amounts of information, allowing a computer to compare a patient's sy...

More Accessible Expertise

Smart machines can perform faster and more accurately than humans, but they don't necessarily use the same processes. Consider an example from the legal field. When there is an unresolvable dispute between two parties, the dispute can go to court. Re...

Collective Expertise

We tend to think of professional work as being conducted by experts—people who hold degrees certifying their expertise and whose practices are defined by their profession. However, large numbers of people making small contributions have the power to im...

Implications for Higher Education

Smart machines are reconfiguring professional work. Although the thought of tasks being performed by a machine can be disquieting, who performs the task is less important than the outcome. Is the task done better by "man" or "machine"? Robots can be mo...

Finding and Developing Human Expertise

As our professions are changing due to smart machines, big data, and robots, the capabilities we look for in professionals—and how those professionals are selected and advanced—are changing as well. AI is playing a critical role in this shift.Virtually all position descriptions are advertised online. Credentials (e.g., resumes, transcripts, and test results) are available in digital format, making it possible for natural language processing and big data to power talent analytics platforms. Massive amounts of data can be aggregated and analyzed to gain new insights. For example, employers are using talent analytics to answer questions such as "What are the characteristics of employees who are being promoted?" Quality-of-hire analysis helps answer "What skills do they have?" and "Where and how did they learn this skill?"16 Detailed analyses of the competencies associated with professional success are informing position descriptions and enabling competency-based hiring.Machine learning and AI are being used in nearly all phases of talent recruitment:New ways to assess and credential professionals' qualifications, communicate those qualifications to the market, and match talent with positions are emerging. Badging is just one example.

Performance-Based Assessment

Historically, the best indicator of intellectual and interpersonal skills was a degree. However, supplementing the degree by measuring ability through performance simulations may result in better matches between candidates and positions. EquitySim, for example, is a simulation platform that helps employers identify top talent for entry-level financial trading positions. Because it is performance-based, it helps reduce biases, such as those associated with age or gender. The platform can also identify talent at institutions where employers don't usually recruit. The simulation involves having the user trade stocks, bonds, currencies, and securities, yet the data collected is much richer. The platform captures more than 100,000 behavioral data points per user (e.g., order of steps followed, duration of time spent). These data points are associated with important characteristics, such as risk management capability, in order to target candidates based on competencies that are predictive of success and retention. Although the simulation measures performance, success in the simulation can result only from critical thinking, problem solving, and other core cognitive skills. When companies use EquitySim, 48 percent of candidates hired are female, compared with 25 percent when using traditional approaches.20

Transferable Skills

A fusion of education, training, and experience will be required for the long-term career growth of tomorrow's professionals. A 2016 survey found that 54 percent of Americans believe it will be essential and 33 percent believe it will be important for them to develop new skills throughout their career to keep pace with changes.21 Competencies will be key for the future of professionally oriented education because many of them (e.g., problem-solving, collaboration) are common across industries. Sustainable career paths depend on transferable skills and competencies. Although the term "soft skills" is often used to describe problem-solving, communication, collaboration, critical thinking, and teamwork, a better term may be "mobility skills" because they enable individuals to move from one position to another.The options for developing these skills are all around us: competency-based education, apprenticeships, internships, certificates, boot camps, and badges. Do-it-yourself learning opportunities are available online, all the time. Stackable credentials offer learners pathways from today's jobs to tomorrow's. Whether skills and competencies are developed at a college, university, or corporation or were self-taught matters less than the ability to transfer that expertise to new problems. Employers are interested in "agility"—the ability to adapt rapidly and on an ongoing basis.22 As a result, the adoption of approaches such as badging is growing among both professionals and employers.

Co-Created Degree Alternatives

If training and education must become more frequent across a career, blocks of courses in two- or four-year degrees will be too inflexible for a continuous work-learn model that may span forty to fifty years. Alternative models are emerging.One example of a blended work-and-learn model is Northeastern University's professional master's degree programs that provide pathways from IBM badges to academic degrees. Under IBM's "New Collar" program, 15 percent of its skilled jobs are now held by workers without college degrees. Badges are being used as a way to develop skill and verify talent with "competency stacks." IBM has issued more than 850,000 badges. When Northeastern administrators reviewed the badges, they found many that could be applied toward academic degrees. To date, 3 professional master's programs build on IBM badges, with another 51 degrees and 17 certificate programs under consideration. Other large employers that have recently announced badging programs for talent development or competency-based hiring include Microsoft and Ernst and Young.23

Integrating Credentials Online

Learning and credentialing take place on the job as well as at colleges and universities. Hundreds of organizations offer an estimated 250,000 credentials such as badges, micro-master's, certificates, and degrees.24 Now that credentials are digital, on...

Implications for Higher Education

How will higher education (either through degree programs, certificates, or continuing education) help ensure that millions of new and existing professionals have the skills to transition to new positions? Clearly education is a critical component of how society manages the massive disruption smart machines represent. But to what extent does that future education look like today's? In the near term, changes may be felt by college/university career planning and placement offices as students prepare to enter the job market. But it is not only new college graduates at the start of their career who need assistance in developing their talent; this shift includes adults at varying stages of professional and lifelong learning.27 Longer-term impacts revolve around the transparency of what a degree signifies. Students invest in credentials to advance their careers. Though the significance of a degree and the power behind a college or university brand are likely to remain, data-driven and competency-based approaches will challenge higher education institutions to provide greater transparency into what graduates can do, both on graduation and throughout their career. Ultimately, talent platforms will enable employers and educators to better align professional demands with educational options.28 Higher education leaders should ask questions such as the following:

Challenges for Higher Education

AI and other technologies will find their place in higher education. Today chatbots are responding to questions about registration, course availability, and homework assignments. AI is already conserving resources and saving money for colleges and un...

Notes

Diana G. Oblinger is President Emeritus of EDUCAUSE.