Utilizing Digital Human Technology to Attract International Students and Promote K-beauty Bepartments
Bibliographic record
Abstract
Korea is actively pursuing various initiatives to become a global leader in education that can meet the current demand for education and attract and nurture international talent to drive regional and institutional growth. Developed countries such as the U.S., U.K., Australia, Germany, and Canada are competing to attract international students to enhance their national competitiveness and secure talent from abroad. Korean universities are similarly focused on addressing the challenges posed by a declining school-age population and are increasingly targeting international students who can contribute positively to the local economy. The purpose of this study is to explore the use of digital human technology in attracting and promoting international students who wish to enroll in Korean universities to study K-beauty. To do so, we first analyzed the current status of international students in Korean higher education institutions based on statistical data from the Ministry of Education, Statistics Korea, Korea Educational Development Institute, Ministry of Employment, and Ministry of Justice, including the number of international students by course, discipline and region, country of origin and region, type of study abroad, university, and major, the share of international students in the world, the number of international students, and the status of improving the visa system for international students. In order to utilize digital hobnobbing technology to promote departments to attract international students, we utilized a tool provided by Klleon. From 2015 to 2022, the number of foreign students in domestic higher education institutions increased from 91,332 in 2015 to 166,892 in 2022. Four-year institutions accounted for 149,576 students, 89.6%, vocational colleges 8.7%, and graduate schools 1.7%, and by region, 58.2% were in the metropolitan area and 41.8% were in non-metropolitan areas. By country of origin, China accounted for 66,372 (43.6%), and Asia accounted for 90.8%. In terms of non-metropolitan areas, Busan, Daejeon, Chungnam, and Jeonbuk were found to be higher than other regions. By university, Hanyang University, Kyung Hee University, and Sungkyunkwan University were the top three, with humanities and social sciences accounting for the highest proportion. Although the share of international students in Korea has been slowly increasing, the share in 2020 was 2%, which is still lower than the OECD. For international students who complete their studies in Korea, employment after graduation is very low, and the Ministry of Justice is working to improve the system for international student visas. As the interest in digital humans in education is increasing, and the technical support for creating digital humans is getting cheaper, faster, and more convenient, it is expected that their use in attracting and promoting international students will increase. As the social interest in generative AI has recently increased and the field of digital beauty has been established as an academic field, it is predicted that the day may soon come when AI humans will be used in beauty education, so it is necessary for instructors to prepare for this.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".