q & A with
zoran popović

For Horizons June 13–14, 2018  |  New Orleans, LA

In anticipation of our national summit, Horizons, which will bring together practitioners and policymakers in workforce and education, JFF had a chat with one of the attending keynote speakers, Zoran Popović.

 

Popović is a professor of computer science at the University of Washington, the director of its Center for Game Science, and the founder of Enlearn, a software company that has developed a personalized learning platform to encourage positive change in education. We were excited to get his thoughts on the summit and the future of learning.

 

Given the unique mix of attendees at Horizons, what inspired you to speak about the future of technology and its impact on learning and work to this group?

Tech education is still largely an unrealized promise. We know what technology can do, and it makes sense that it can revolutionize education, but it hasn't really happened. I am looking forward to sharing with the audience some of the key reasons why things have not improved as much as we wanted, why I think the next 10 years are likely to bring drastic improvements, and how the latest research and products we have deployed at scale point to an emerging quantum leap in learning.

 

Why do you think artificial intelligence (AI) and other technological innovations, such as machine learning, hold promise to improve learning in the future?

Machine learning can expand beyond content mastery, first by considering the entire person—what drives them, what their concerns and fears are, and their learning strategies and attitudes toward learning. Even further, my research shows that once we bring in the entire ecosystem of a person—their peers, teachers, classrooms, and parents—the potential to make a big change through technology is exponentially larger.

 

Modern AI techniques can also help to create on-the-fly learning experiences for every individual. We still think of content as monolithic sections of a textbook that every student needs to go through or large courses that students need to pass. My research has recently shown that there are completely different variations of algebra knowledge that are more suitable to various students, ranging from students who have not benefited from strong K–12 schools, to advanced students, to those pursuing a nursing degree. Soon, AI will be able to generate the right knowledge, curriculum, and progression to uniquely suit the needs of learners as well as the workforce.

 

We understand you spend a lot of time thinking about how technology can support apprenticeships. Can you share some of your vision?

Technology advancement will soon allow us to achieve targeted apprenticeship at scale. We know that the highest levels of expertise are primarily gained through apprenticeships, but those models simply do not scale to thousands of students. In many ways, our current schooling models are failed attempts at replacing apprenticeships with classroom structures. With the emergence of augmented reality and increasing expertise in modeling, we will soon be able to support students as they perform the exact skill they are interested in, real or virtual, rooted in [their] context of interest, while AI generates just-in-time feedback to dramatically accelerate mastery.

 

How does Enlearn and its solutions alleviate some of the pessimism about preparation for the future of work?

Enlearn tries to ask different questions than the standard technological solution. We realize the irreplaceable value of a teacher and don't try to replicate the teacher with technology but instead ask, what can the state-of-the-art algorithms do that can augment and amplify teacher skills?  Enlearn’s platform is being designed to provide actionable answers to the following types of questions and in the process provide a brand new learning experience:

  • What alternate learning structures are possible, relying on strengths that technology can uniquely provide?
  • How can we understand each unique learning ecosystem and use technology to improve the existing structure? 
  • How can we construct a course that is tailored to uniquely suit the needs of each individual?
  • Can we change aspects of the curriculum—on the fly—based on what we learn from the student?
  • How can learning be changed to consider the entire person, including their preferences, interests, fears, confidence, etc.? 
  • How can a learning environment change learner mindset, grit, and learning agency?

 

What are your thoughts on how AI and other technological innovations will impact the skills in demand in the future and the future of work?

By 2030, we will be closer to realizing the goal of practically any person being able to learn any skill rapidly. People will have full confidence in their abilities to attain skills because they will have done so regularly, and technology will aid optimal ways for each individual to learn. Because jobs will continue to rapidly evolve, putting new demands on the existing workforce, these changes will be necessary. Re-skilling will happen frequently and constantly, putting pressure on standard K–12 and college learning pipelines. The grades you got and the college you went to will matter a lot less than how adaptable and driven you were during past learning experiences and how well you can pivot to new skills. As a result, non-cognitive skills, such as agency, mindset, struggle, and metacognitive skills, such as productive exploration, problem-solving skills, and resourcefulness, will be the primary predictors of job success—not grades and degrees.

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