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Session Submission Summary

AI & Big Data based Future Education Policy in Korea

Fri, April 22, 6:00 to 7:30am CDT (6:00 to 7:30am CDT), Pajamas Sessions, VR 104

Group Submission Type: Formal Panel Session

Proposal

Education is confronted with a historic moment of socioeconomic and demographic changes including the rapid increase of aging population, decrease of school-age population, and the growing impact of advanced technologies. Today, many countries are required to deal with these challenging issues for their current and next generations. Especially, the outbreak of the COVID-19 pandemic is accelerating the digital transformation in the field of education, and the unprecedented development of the artificial intelligence (AI) and big data technologies is at the center of the transition.
A concrete case is Korea, the only OECD country with a total fertility rate of less than 1 (0.92). Due to low birth rates and increased life expectancy, Korea is rapidly becoming an aged society. Korea became an aged society that had 14% of its population in the 65-or-over group in 2018, and is expected to become a super-aged society that had 20% of its population in the 65-or-over group in 2025. A shrinking number of students in P-20 education and an expanding number of potential students in lifelong learning resulted from these demographic changes are causing a necessity to consider a new form of education system for all levels of students in Korea.
In this regard, Korean government pays attention to the advent of the 4th Industrial Revolution as an inflection point for the renovation of education system. It is expected that the prodigious advance of information and communications technology through the 4th Industrial Revolution will bring about structural changes in industries that overwhelm the existing components of production such as labor and capital and have a tremendous influence on society as a whole. In particular, AI and big data technologies are construed as key factors to analyze the impact of educational policies in reality and provide a highly individualized education service for each student.
Given the context of these socioeconomic and demographic changes, this group session consists of four studies investigating the policy issues of system reconstruction for the future of education in Korea. In the first study, authors indicate that the current education administration system in Korea shows structural limitations in responding promptly to rapid social changes, and suggest a transformation from a standardized control system to a flexible system emphasizing diversity and autonomy of each subject including local government, school, teacher, and student population. To be specific, authors analyze the prospects for changes in the forthcoming educational environment in Korea, explore trends in educational reform and educational governance in foreign countries, and review controversial issues related to the establishment of a future education governance.
Also, in the second study, authors point out that the literature on the development of forecasting models for teacher workforce in Korea has focused on the future demand for teacher based on the forecasting on the total population and the OECD average class size. However, the forecasting models that only focus on the future demand on teacher have failed to consider another important aspect of workforce planning, which is the need for the teachers. For this reason, authors aim to develop forecasting models for teacher workforce planning using system dynamics modeling by considering both the demand and need for teachers. By simulating various factors based on both demand and need perspectives using system dynamics modeling, thus, this work will contribute to the development of more improved forecasting models for teacher workforce planning in contemporary Korean society.
In the third study, authors analyze policy issues for the future of education in Korea to examine the discrepancy between the reflection level and importance of policies. Drawing on interview data from 21 experts on educational policy issues including presidents of learned societies, heads of policy research institution, and professors in the field of education, authors review opinions about the priority of issues on educational policies related to future education, as well as their opinions about the actual level of policy reflection in reality. Analysis shows that there are policy issues with high importance but low level of reflection as follows: reforming the teacher education system including multiple qualifications, realizing personalized education for each student through the use of advanced technology including the AI, building an online and offline education platform for the post-COVID-19 era, re-establishment and re-education of teacher roles for future education, the introduction of AI tutoring system in the public education, and promoting scientific education policies based on the use of the AI and Big Data technologies.
In the fourth study, authors provide approaches to AI-based early detection of dementia and to propose cognitive-linguistic training programs as preventive education for at-risk older population. Specifically, authors demonstrate AI-based methods to detect impaired features of linguistic domains from sentence production in Alzheimer’s disease. As for the prevention program, authors share results from their linguistic-cognitive programs that are applied for people with mild cognitive impairment, which is referred as a transitional stage between normal aging and dementia. This work comprises two stages. First, the approach of a machine-learning is used to analyze the noun-verb semantic networks for individuals with Alzheimer’s Disease. Second, an investigation into the treatment efficacy of story-based working memory training for individuals with amnestic type of mild cognitive impairment is conducted.
With regard to the socioeconomic and demographic changes in Korean society, these four studies address a broad range of challenging issues including a new form of education governance, forecasting models for teacher workforce planning, prioritization of education policies, and educational treatment for at-risk older population from the perspective of digital transformation in the field of education. These studies have significance in that they contribute to a better understanding of how we can utilize AI and big data technologies to cope with the challenges in contemporary society. Further, given that the changes are not just a regional trend in a specific area but a global issue observed in many countries, these studies also have implications as an opportunity for the broader and deeper discussion on the utilization of advanced digital technologies for the future of education.

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