The Challenges Technologies Bring to Higher Education
Alexa talks to you. Google finds answers to your queries. Amazon knows your preferences. Facebook not only knows your friends but also can help you find the perfect partner. These platforms seem to know what we are thinking almost before we do. Our world has taken on a digital smartness thanks to artificial intelligence (AI), data, natural language processing, automation, and robots that, although nearly invisible, impact much of what we do.
This digital smartness is projected to have a massive influence on the world economy, adding $15.6 trillion to global GDP by 2030. It will increase productivity and wages, allowing individuals to purchase more and/or better products. Automation, driven by AI and robotics, is estimated to require the reskilling of one-third of the 2030 US workforce, with nearly 10 percent of positions being in fields unknown today.
If “smart machines” are having such impact on the economy and our professions, what will they mean to higher education? For example, could a chatbot be your next TA? “Jill Watson,” the first chatbot teaching assistant for an AI course at Georgia Tech, responded to students’ questions so well that some students wanted to nominate “Jill” as the best TA in the course.
At Beckett University in the United Kingdom, chatbots help prospective students find available courses of study. Georgia State University (GSU) uses an AI chatbot to respond to questions about enrollment and financial aid, handling peak volumes of as many as 2,200 calls per day, with 220,000 questions answered in the first summer of use. (When the system is less than 96 percent confident of an answer, the query is passed on to a staff member.) But the impact goes beyond handling call volume.
GSU estimates that the timely responses to questions helped reduce “summer melt” (i.e., the loss of students who are admitted but not yet registered) by 21 percent. Deakin University in Australia has created a platform, Genie, that combines chatbots, AI, voice recognition, and a predictive analytics engine to create an intelligent virtual assistant that provides students with advice. Chatbots are being tested as English tutors as well.
In spite of their growing use, digital assistants only scratch the surface of the coming changes. Colleges and universities are challenged to move beyond the use of technology to deliver education. Higher education leaders must consider how AI, big data, analytics, robotics, and wide-scale collaboration might change the substance of education. The world around us is getting smarter. What does it mean to be a professional in a world of smart machines?
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, notes gatech.edu. Machine learning allows computers to “consume” information such as medical records, financial data, purchases, and social media and then develop predictions or recommendations. Today’s AI uses “brute force” computing, enabled by massive amounts of data, memory, and processing power.
Beyond processing instructions at incredible speed, these machines can create their own guidelines and discover patterns invisible to humans. AI allows IBM’s Watson, for example, to aggregate clinical guidelines, medical literature, and patient data to help physicians diagnose and treat cancer. AI and imaging software can speed up the diagnosis and treatment of strokes.
Digital smartness comes in other forms too. In health care, robots allow surgeons to perform precision surgery. Collaborative robots in e-commerce fulfillment facilities help workers “pick” (i.e., select) items two to three times faster and with close to 100 percent accuracy (see educause.edu). ROVs (remotely operated vehicles) allow humans to explore other planets, collect data in active volcanos, and search for victims in a burning building — among other tasks. Robotic prosthetics and exoskeletons help amputees and those with impaired mobility.
Self-driving cars and trucks promise to make transportation more efficient. Drones, essentially flying robots, have an increasing number of uses, from firefighting to farming, with an estimated value of $124 billion. In-space manufacturing and assembly is being explored using autonomous robots and additive manufacturing techniques (i.e., 3-D printing).
Today’s robots interact with the physical world. Robotic sensing gives machines the ability to “hear” through signal processing, “see” through image processing, and “touch” through pressure and pattern processing. In addition, this generation of robots can detect and express emotions. Social companion technology, in which a machine displays empathy, is being explored for the elderly to help combat loneliness as well as monitor wellness. These part-robot, part-AI systems use animatronic gestures and “speak,” providing information, reminders, and support as they adapt to and learn from their human companions.
If smart machines can take on all these human tasks, what does that mean for people? Will we need to know or do less — or more? And with these smart machines having such a large impact on the economy and the workforce, what will they mean for higher education? Rather than replacing people, smart machines augment human capabilities, meaning that we need to learn to work with machines as partners. Changes in our professions are becoming more rapid, suggesting that the way we develop and find expertise will change as well.
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.”
As 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 850,000 lower-skilled jobs (e.g., call centers) while simultaneously creating 3.4 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.
Thus, whether it is AI, robotics, or another technology, today’s machines can work alongside professionals as partners, amplifying human performance and augmenting human intelligence.
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 more precise and reliable in advanced manufacturing or medicine, for example. However, just because a task can be performed better by a machine does not mean a job goes away. A robot that sutures a patient does not replace the surgeon. Low-level tasks performed by humans can be replaced with higher-level ones.
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?” Detailed analyses of the competencies associated with professional success are informing position descriptions and enabling competency-based hiring.
A fusion of education, training, and experience will be required for the long-term career growth of tomorrow’s professionals. A 2017 survey found that 52 percent of Americans believe it will be essential and 36 percent believe it will be important for them to develop new skills throughout their career to keep pace with changes. 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. As a result, the adoption of approaches such as badging is growing among both professionals and employers.