SCENARIO ANALYSIS OF THE IMPACT OF THE INTEGRATION OF GENERATIVE ARTIFICIAL INTELLIGENCE ON INSTITUTIONAL TRUST IN EXECUTIVE EDUCATION PROGRAMS

Abstract

The active introduction of generative artificial intelligence (GenAI) into the educational programs of business schools highlights the problem of the impact of technological saturation on the perception of the quality of education and the reputation of educational institutions. In the Executive Education segment, focused on managers, this problem remains insufficiently studied. The purpose of the study is to evaluate the impact of various levels of GenAI integration into Executive Education programs on the institutional trust of students. The hypothesis that a high level of technology will increase trust has been tested experimentally. The scientific novelty lies in the identification of a paradoxical inverse relationship between the intensity of GenAI use and trust in the educational program among this category of respondents, as well as in demonstrating the role of previous experience working with artificial intelligence. The methodology is based on an experimental design with a scenario approach: 60 respondents were randomly assigned to three scenarios that differ in the level of integration of GenAI into the training program (high, medium, minimum), after which they assessed their attitude to the program on a 5-point Likert scale. The results showed that the maximum level of trust (3.8) and willingness to recommend the program (3.8) corresponded to the traditional scenario with minimal use of GenAI, while the high-tech scenario received the lowest ratings (2.8). It was found that 37% of respondents have never used GenAI, and this group shows the lowest level of trust (2.86), while active users rate trust significantly higher (4). Skepticism about GenAI's ability to improve the quality of education is recorded in all scenarios (average grades below 3). The practical significance lies in substantiating recommendations for balanced integration of GenAI as an optional auxiliary tool that does not displace traditional methods, and the need to demonstrate its usefulness and safety in order to overcome barriers of distrust. The prospects for further research include conducting longitudinal studies, expanding the sample, and analyzing age and occupational differences in the perception of generative artificial intelligence technologies.