ANALYSIS OF APPROACHES TO THE INTEGRATION OF ARTIFICIAL INTELLIGENCE METHODS INTO INTELLECTUAL CAPITAL MANAGEMENT

Abstract

The article examines approaches to integrating artificial intelligence methods into intellectual capital management systems of higher education institutions in the context of forced migration and organizational transformation. A comparative analysis of the US market-oriented approach and the EU human-centric approach is conducted, revealing fundamental differences in the philosophical foundations of artificial intelligence implementation in human resources management. The research proposes a hybrid model that combines technological flexibility of American People Analytics with European ethical standards, adapted to the specific challenges faced by Ukrainian higher education institutions. The study analyzes key artificial intelligence technologies: Organizational Network Analysis for diagnosing integration and identifying isolated groups, Natural Language Processing for monitoring psychological well-being and early detection of burnout, and Learning Experience Platforms for personalized professional development. Machine learning models including classification, clustering, regression, and optimization algorithms are proposed for workload redistribution and performance prediction. The developed approach integrates exploratory data analysis, predictive modeling, and optimization techniques (convex optimization and genetic algorithms) with fuzzy logic to ensure transparency and interpretability of managerial decisions. The proposed model enables higher education institutions management to proactively forecast risks, optimize resource allocation, segment staff for targeted support, and fairly evaluate performance considering the forced displacement context.