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AN AI-DRIVEN PREDICTIVE MODEL FOR SCREENING LEGAL PROFESSIONALS IN INTERNATIONALLY ORIENTED UKRAINIAN IT COMPANIES
(Manuscript, 2026) Dzoban, Volodymyr
This applied research remedies a pressing talents issue for legal departments in Ukrainian IT companies because typical credentials are poor predictors for success in a fast-moving technology sector. The author created and tested a screening tool based on large language model and statistical modelling techniques to better evaluate candidates in an initial assessment stage. Using a dataset of 269 legal professionals who were 178 candidates for a major Ukrainian IT company and 91 professionals in the LinkedIn networking group, this study applied logistic regression analysis and narrative coding to explore predictors for success measured as acceptance of a job offer, retention for 24 months, and delivery of satisfactory performance.
Results demonstrated that a background in the information technology industry, English language skills, foreign transaction experience, interest in technology, and a business focus were strong predictors for success, while traditional credentials such as university name, prior employment with a law firm, and membership in a bar association lacked predictive power. The model has 78.4% classification accuracy with 81.2% sensitivity and 75.6% specificity in cross-validation with AUC=0.78. It was used for creating a screening tool based on GPT for candidate screening where output classifies candidate data into organized assessments with scoring points for strong traits, areas of concern, and interview recommendations. Pilot testing showed a 60% reduction in screening time while maintaining quality.
This study addresses a repeatable approach for statistical model development in the context of a particular legal staff environment and a usable screening tool for the technology sector legal recruitment. The results of this research challenge traditional forms of credentialism associated with legal recruitment and establish that culture fit factors potentially outperform legal credentials.
MANAGEMENT TRANSFORMATION IN THE UKRAINIAN VACATION SHORT-TERM RENTAL MARKET: LEVERAGING AI TOOLS FOR MARKETING AS A COMPETITIVE ADVANTAGE AND RESOURCE OPTIMIZATION
(Manuscript, 2026) Danchak, Nestor
The rapidly growing artificial intelligence industry is transforming hospitality operations, with applications spanning automated customer service, content generation, and workflow optimization. While large hotel chains have more resources to adopt enterprise AI solutions, small vacation rental operators managing cabins, cottages, and nature-based properties face critical challenges: dependence on online travel agencies (OTAs) extracting 15-25% commission fees while controlling customer relationships, combined with resource constraints limiting
competitive response capabilities.
This capstone develops a management framework enabling small Ukrainian vacation rental operators to leverage accessible AI tools for marketing automation, platform independence, and resource optimization. The research employs qualitative methodology: empathy mapping and
customer journey analysis. Applying them across nine in-depth interviews with vacation rental guests. Analysis identifies five distinct guest segments (Aesthetic Sensualists, Comfort Planners, Nature Explorers, Festive Socializers, Retreat Seekers) and three universal friction points:
insufficient online brand presence, slow communication response times, and content-audience mismatch.
The resulting framework integrates four automated components implemented through a phased 12-week roadmap, reducing weekly operator time to 2-3 hours while targeting 30-40% direct booking conversion within six months. While specific AI tools evolve rapidly (ChatGPT, Make.com, and Google Vision AI may be superseded), the framework emphasizes goal-oriented methodology: defining clear key performance indicators (KPIs), validating tool effectiveness against strategic objectives, and continuously adapting technology choices to serve business outcomes rather than pursuing technology for its own sake.
This research proposes an actionable framework enabling resource-constrained operators to potentially achieve competitive advantages through strategic AI adoption. The framework design suggests significant time savings and platform independence opportunities.
ЗВІТ БІБЛІОТЕКИ ЗА 2025 РІК
(2026) Гужва, Алла
USE OF ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF SECONDARY EDUCATION INSTITUTIONS IN UKRAINE
(Manuscript, 2026) Vyslynskyi, Bohdan
The management of secondary education institutions increasingly relies on digital tools to support administrative decision-making and organizational processes. While Artificial Intelligence (AI) has been widely discussed in the context of teaching and learning, yet its use in secondary school management remains underexplored. This study examines institutional readiness, current patterns of AI use, and perceived risks of AI adoption in the management of secondary education institutions in Ukraine.
Using a mixed-methods approach, the study combines data from an online survey of 43 urban school administrators with insights from ten semi-structured interviews. The findings show a state of moderate readiness, as most schools indicated some level of infrastructure and strategic planning for AI integration. However, only a few cases of systematic use of AI have been reported in administrative practices. In most cases, respondents apply AI in isolated instances or pilot projects. AI applications are therefore most common in relatively simple administrative areas, such as scheduling
and resource allocation, while their use remains rare in more complex areas, including decision-making and performance monitoring.
The main barriers to wider AI adoption include concerns related to data protection, regulatory uncertainty, insufficient funding, and limited staff capacity. These findings suggest that effective AI integration in school management requires clear governance frameworks, targeted professional development, and upgrades to digital infrastructure.
MSMES AS A KEY DRIVER OF UKRAINE’S RECOVERY: EVALUATING SUPPORT PROGRAMS, COMPARING INTERNATIONAL PRACTICE, AND CREATING A BANK-HUB STRATEGY
(Manuscript, 2026) Zhulanova, Yuliia
This capstone is a consulting engagement that addresses three applied questions: (1) what the available market and program evidence suggests about the effectiveness of Ukraine’s MSME support instruments in 2022–2024; (2) how these instruments compare with historical and institutional lessons from South Korea, Israel, Japan, and the Marshall Plan countries; and (3) how a bank-based delivery approach can be structured to scale support under high risk and uncertainty.
The analysis relies on practical evidence from operational statistics, program and administrative reporting, and donor/IFI documentation. Because inputs are fragmented across sources, results are synthesized through a unified KPI logic—drawing on OECD and World Bank evaluation approaches adapted to wartime conditions—and presented in comparative tables and appendixes. Oschadbank is used as a case study with a significant market role to validate and illustrate program findings at the bank level. The work also uses historical context to refine instrument design choices and to translate international experience into feasible steps for Ukraine.
Results show that Ukraine deployed a broad toolkit dominated by subsidized lending and guarantees: over 100,000 loans were issued under “Affordable Loans 5-7-9%” (several hundred billion hryvnias), and over 30,000 loans were covered by state portfolio guarantees. Donor and hybrid facilities increase leverage and program quality (approximately 2–4×). The Oschadbank case indicates strong scaling capacity: its MSME loan portfolio increased more than threefold in 2022–2025, with around UAH 65 billion disbursed, supporting the feasibility of consolidating delivery through a Bank-Hub approach. Scenario simulations suggest that an MSME-focused Bank-Hub envelope of EUR 300 million+ could support several thousand additional investment projects and mobilize over EUR 1 billion when combined with IFI risk-sharing. The capstone concludes with an action-oriented plan to priorities instruments, rebalance support toward risk-sharing and blended finance, strengthen unified data and monitoring, and add a venture/equity pillar for innovative and high-growth firms.