Deep learning in education is fairly a new concept. Although the concept of predictive analytics is being slowly introduced in predicting student performance based on numerous data points, making learning experiences more adaptive, individualized, or engaging has not been achieved. Furthermore, integrating higher order learning activities (i.e., applying knowledge, problem solving, critical thinking rather than memorization) or assessing student performance beyond standardized tests has not been done to date. Unfortunately, such phenomenon is solely due to the fact that tests and exams have been dictating how learners learn. In short, deep learning can help replace all standard exams while making learning more engaging and highly interactive in all forms of education and training scenarios. In the long run, deep learning can and will help transform the way students and trainees learn and the way they are assessed with higher order thinking activities.

Paul Kim is the Chief Technology Officer and Assistant Dean at Stanford University Graduate School of Education. For the past 18 years, he has been advising numerous educational institutions and participating in a series of major M&A, due-diligence, and corporate finance engagements in the areas of education technology, data analytics, online training, and technology infrastructure. He also served as an advisor to National Science Foundation, National Academy of Sciences, and WestEd. He has developed and taught graduate-level courses and MOOCs related to educational entrepreneurship, technology design, and international development. His recent government-backed development projects include the design of a new science and technology university for the Sultan of Oman, the strategy design for the national online learning initiative of Saudi Arabia, and the national education technology assessment initiative for Uruguay