The Use of Virtual Reality in Art Education in Ukraine: A Study of the Impact on the Creative Process and Students' Perception
Bibliographic record
Abstract
Purpose: The study aims to explore the role and application of virtual reality in art education in Ukraine, to analyse the impact of this technology on the creative process of students pursuing higher education in the field of fabric and clothing design. The aim is also to provide a brief overview of the principles of modern art education in Ukraine and to highlight the role of virtual reality in education, including design, based on empirical research. Methodology: The study involved 37 students studying at the Department of Textile and Clothing Design, who are studying certain disciplines in the Conceptual Design programme. Quantitative and qualitative analysis of the data obtained was used, including statistical analysis in characterising the frequency of use of virtual reality technologies and qualitative thematic analysis of interviews. Results: The study indicates the active use of virtual reality in art education in Ukraine, in particular, it highlights that 86.5% of students consider the impact of virtual reality on their creative process to be positive, and 89.2% believe that it improves their conceptual design skills. The study also revealed the benefits and challenges of using artificial intelligence and virtual reality in art education. The conclusions emphasise that modern Ukrainian art education actively takes into account technological trends, using new digital learning solutions. The results show a positive impact of virtual reality on the creative process and conceptual design skills of students. However, the use of artificial intelligence and virtual reality raises ethical and technical challenges that need to be carefully addressed for maximum benefit and harmony with the artistic process.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".