Template-Type: ReDIF-Article 1.0 Title: AI-Driven Vehicle Evaluation for Online Used Vehicle Auctions Author-Name: Mykola Bielousov Author-Workplace-Name: Master of Science in Computer Science, State University of Information and Communication Technology, Kyiv, Ukraine, IEEE Senior Member (#102065992), IEEE Computer Society, Full-Stack Software Engineer, CodeSoft LLC, 1376 Stonewood Ct., San Pedro, United States Abstract: The aim of this study is to develop and substantiate an approach to the valuation of used vehicles within online auction environments using artificial intelligence methods, taking into account the specific characteristics of the United States and European Union markets. The study employs a comprehensive set of methods, including the analysis of contemporary scientific approaches, comparative analysis of market models, and machine learning techniques to formalize the valuation problem as a regression task. Particular attention is given to the use of multimodal models that integrate structured, semi-structured, and unstructured data, as well as to approaches for uncertainty estimation and result interpretability. The obtained results confirm that traditional valuation methods have limited effectiveness in digital auction environments, whereas the application of AI significantly improves prediction accuracy, reduces the impact of information asymmetry, and enhances decision-making validity. It has been established that the integration of visual data and historical information is a key factor in improving model performance. It is concluded that the use of multimodal AI approaches, combined with uncertainty estimation and interpretability, provides a solid foundation for the development of effective decision support systems in the field of online vehicle auctions. Journal: Contemporary Issues in Artificial Intelligence Year: 2026 Volume: 2 Issue: 1 File-URL: https://sciformat.ca/journals/index.php/ciai/article/view/46/38 File-Format: application/pdf DOI: 10.69635/ciai.2026.46 Keywords: Artificial Intelligence; Vehicle Valuation; Machine Learning; Online Auctions; Multimodal Models; Information Asymmetry Handle: RePEc:cvj:ciai01:v:2:y:2026:i:1:n:1 Template-Type: ReDIF-Article 1.0 Title: Artificial Intelligence in Legal Practice: A Comparative Analysis of Regulatory Frameworks in Ukraine and the United States (2023-2026) Author-Name: Kateryna Klymenko Author-Workplace-Name: LLM Candidate, Chicago-Kent College of Law, Illinois Institute of Technology, Licensed Attorney (Ukraine, 2009), Sarasota, Florida / Chicago, Illinois, USA Abstract: Purpose: This article undertakes a systematic comparative analysis of artificial intelligence regulation in legal practice across two jurisdictions-Ukraine and the United States-during the period 2023-2026. The study examines how each legal system has responded to the integration of generative AI into attorney practice, with particular attention to professional ethics obligations, judicial precedent, executive action, and harmonization trajectories. Methodology: The study employs comparative legal analysis, doctrinal examination of primary sources (legislation, professional conduct rules, formal ethics opinions), case law review, and systematic analysis of regulatory instruments across both legal systems and EU law. Findings: Neither jurisdiction has enacted comprehensive AI-specific legislation for legal professionals as of 2025. The United States has developed more specific professional standards through ABA Formal Opinion 512 (2024) and a body of judicial precedent anchored by Mata v. Avianca (2023). The Executive Order of June 2, 2026, introduced a voluntary federal AI oversight mechanism while affirming the administration's preference for industry self-regulation. Ukraine is pursuing a bottom-up regulatory approach aligned with EU AI Act obligations. A shared regulatory gap in both systems concerns AI in mediation and ADR. Conclusions: Both systems rely on general professional ethics principles as their primary regulatory instruments. They diverge significantly in the specificity of guidance, existence of judicial precedent, and regulatory harmonization trajectory. The article formulates concrete recommendations for NAUB, Ukrainian legislators, and US bar associations. Journal: Contemporary Issues in Artificial Intelligence Year: 2026 Volume: 2 Issue: 1 File-URL: https://sciformat.ca/journals/index.php/ciai/article/view/47/39 File-Format: application/pdf DOI: 10.69635/ciai.2026.47 Keywords: Artificial Intelligence; Legal Regulation; Attorney Competence; ABA Model Rules; EU AI Act; Attorney-Client Privilege; LegalTech; Comparative Law; Ukraine; United States; Mediation; Generative AI Handle: RePEc:cvj:ciai01:v:2:y:2026:i:1:n:2 Template-Type: ReDIF-Article 1.0 Title: The AI-Driven Transformation of Digital Marketing: Evolving Customer Journeys, Predictive Analytics, and Novel Acquisition Channels Author-Name: Vladyslav Bilinchuk Author-Workplace-Name: Founder, ILION DIGITAL LLC, USA; Expert in Performance Marketing and Ecosystem Integration; Master of Business Management, Kyiv National University of Trade and Economics, Ukraine Author-Name: Oleksandr Korogovnyi Author-Workplace-Name: Managing Partner at iLION Digital Agency, USA; Expert in Digital Marketing; Master of Business Management, Kyiv National University of Trade and Economics; MIM-Kyiv Pre-MBA, Ukraine Abstract: This study offers an analytical critique of the structural reconfigurations within the digital advertising architecture precipitated by the convergence of machine learning pipelines and Large Language Models (LLMs). By tracing the dissolution of traditional, linear Customer Journeys, the investigation explores how consumer behavior is restructuring around decentralized, conversational inquiry environments. In tandem with examining the economic pressures facing legacy index-based search marketing, this paper introduces a unified programmatic framework developed at ILION DIGITAL that automates cross-channel data ingestion, predictive anomaly diagnostics, and continuous campaign optimization. Empirical evaluations conducted within hyper-competitive vertical markets across the United States and Ukraine focusing heavily on E-commerce and specialized local services such as Medical, Construction, and Home Services demonstrate that the deployment of unified AI architectures systematically minimizes Customer Acquisition Costs (CAC), prevents rapid budget depletion, and enhances Return on Ad Spend (ROAS). Ultimately, these findings indicate that strategic automation loops do not obsolesce human agency but instead elevate the marketing professional from an operational technician to a core strategic architect. Journal: Contemporary Issues in Artificial Intelligence Year: 2026 Volume: 2 Issue: 1 File-URL: https://sciformat.ca/journals/index.php/ciai/article/view/48/40 File-Format: application/pdf DOI: 10.69635/ciai.2026.48 Keywords: Artificial Intelligence; Large Language Models; Digital Advertising; Customer Journey; Predictive Analytics; Campaign Optimization; Customer Acquisition Cost Handle: RePEc:cvj:ciai01:v:2:y:2026:i:1:n:3