The study aimed to compare the effectiveness of specialists before and after the introduction of digital technologies. The research methodology included experimentation and comparative analysis using learning management systems, cloud-based collaboration platforms, and artificial intelligence in recruitment. The study results demonstrated that digital platforms and automated systems significantly improve the learning process, especially in key areas such as recruitment, onboarding, performance evaluation and development of required competencies. With the use of artificial intelligence and big data, companies can identify training needs with greater accuracy, conduct a more detailed analysis of market trends and predict potential risks, which improves the quality of strategic decision-making. With these technologies, educational institutions can introduce modern teaching methods based on individualised approaches to students and professionals. One of the important advantages of digital solutions is the increased satisfaction of students and employees due to flexible study schedules and distance learning opportunities. This significantly improves internal communication and reduces stress among participants in the learning process. The study determined that such platforms also increase students’ loyalty to educational institutions, contribute to their efficiency and allow them to adapt more quickly to the conditions of the modern labour market. The findings create new opportunities for the further development of training programmes in the field of human resources management. In particular, the integration of digital platforms and automated learning systems helps to develop educational strategies that meet the challenges of the digital economy. As a result, specialists become competitive and prepared for the real challenges of their professional activities
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Volodymyr Nosachenko
Dmytro Nosachenko
Professional Education Methodology Theory and Technologies
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Nosachenko et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69be38356e48c4981c67869b — DOI: https://doi.org/10.69587/pemtt/2.2025.10