COST OPTIMIZATION IN PROFESSIONAL TRAINING: INTEGRATION OF VIRTUAL TEACHERS IN THEORETICAL EDUCATION – AN ECONOMETRIC ANALYSIS
Authors
- Adrian NICOLAU — Athenaeum University, Bucharest, Romania
Abstract
In the context of rising costs associated with professional training and the need for scalable educational solutions, the use of virtual teachers (AI-based) has become an increasingly viable alternative. This article examines the impact of integrating virtual teachers on optimizing the costs of theoretical education, employing econometric methods to assess their efficiency compared to traditional teaching models. The study is based on data collected from professional and technological education institutions, evaluating operational costs, pedagogical efficiency, and the level of acceptance among students and teachers. By employing a multiple regression econometric model, we identify the economic and pedagogical factors that influence the success of virtual teacher implementation, including their impact on academic performance and the cost savings achieved. The findings suggest that integrating these technologies can lead to a significant reduction in educational costs; however, substantial initial investments and a gradual adoption strategy are required. The conclusions provide recommendations for decision-makers regarding the sustainable use of virtual teachers in professional training.
Keywords: virtual teacher, theoretical education, educational costs, econometric analysis, professional training, AI training
Cite this article
APA: Adrian NICOLAU (2025). COST OPTIMIZATION IN PROFESSIONAL TRAINING: INTEGRATION OF VIRTUAL TEACHERS IN THEORETICAL EDUCATION – AN ECONOMETRIC ANALYSIS. Internal Auditing & Risk Management, Vol. 71, No. 71, pp. 44-59. https://doi.org/10.5281/zenodo.15116466
BibTeX:
@article{adriannicolau202520,
title = {COST OPTIMIZATION IN PROFESSIONAL TRAINING: INTEGRATION OF VIRTUAL TEACHERS IN THEORETICAL EDUCATION – AN ECONOMETRIC ANALYSIS},
author = {Adrian NICOLAU},
journal = {Internal Auditing & Risk Management},
year = {2025},
volume = {71},
pages = {44----59},
doi = {10.5281/zenodo.15116466}
}