AI and Digital Transformation in Higher Education: Vision and Approach of a Specific University in Vietnam
Bibliographic record
Abstract
The Fourth Industrial Revolution is opening up new opportunities and challenges for all industries, professions, and fields, aiming to bring humanity more optimal tools and services. During the Fourth Industrial Revolution, digital transformation has been one of the most critical problems. Artificial Intelligence (AI) and the Internet of Things (IoT) are two technologies that have the potential to cause the biggest breakout to evolve in the educational domain. In recent years, digital transformation has seen implementation across all sectors, including education, healthcare, agriculture, transportation, and other smart ecosystems. Among those areas, education, especially higher education, is among the most challenging due to the diversity in training programs, duration, and subjects. The Internet of Things makes it possible to create smart and ubiquitous learning environments, while artificial intelligence can completely transform the way we learn and teach. In this paper, we present the digital transformation process in higher education in Vietnam and internationally and analyze some characteristics of Vietnamese higher education in the digital transformation process. Moreover, we present the vision, approach, and challenges to digital transformation at universities of low- and middle-income countries from the perspective of the Hung Yen University of Technology and Education in Vietnam.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".