ABSTRACT. Welcome message from General Chairs.

ABSTRACT. 20-year retrospection on the ICTERI conference series.

ABSTRACT. This is the welcome message from the Program Chairs of ICTERI 2025

ABSTRACT. Modern system design relies heavily on modeling languages, like UML, which are widely adopted in industry but often disconnected from the formal tools used to analyze and verify behavior. This gap between intuitive design and rigorous analysis limits the practical application of formal methods in everyday engineering workflows.

Multiverse debugging addresses this challenge by treating system behavior as a structured space of possible executions, a “multiverse” that designers can explore interactively. Instead of relying on brittle syntactic transformations, this approach uses a language-independent semantic interface to connect domain-specific models directly to behavioral analysis tools. The result is a scalable, intuitive debugging experience that extends traditional breakpoints with temporal and multiverse-aware breakpoints, enabling fine-grained analysis of concurrency and non-determinism. Applied to UML, and similar languages, this method preserves design intent while providing deep behavioral insights.

This keynote introduces multiverse debugging as a practical bridge between high-level modeling and formal analysis, grounded in a broader vision focused on semantic fidelity and software maintainability across heterogeneous specifications. The approach empowers engineers to better understand, validate, and communicate system behavior, bringing formal reasoning closer to the design desk.

ABSTRACT. The article explores the role of artificial intelligence in the context of three-subject didactics 2.0 – a modern pedagogical concept that redefines the interaction between the learner, the teacher, and the digital educational environment. Based on the three-subject didactics model, the authors analyze the stages of didactics transformation: from a teacher-centered model to an interactive one, and ultimately to the updated three-subject system in format 2.0, where AI becomes an equal participant in the educational process. The article emphasizes the ability of AI to detect logical inconsistencies and analyze dialogues between participants in the educational process to improve the effectiveness of interaction. The paper identifies the levels of interaction between AI and higher education students, analyzes the advantages and challenges of integrating AI into the educational process, and addresses ethical aspects, such as explainability, human involvement, and the risks of reduced social interaction. Special attention is given to the importance of regulatory frameworks and institutional policies, particularly with examples from Ukrainian higher education institutions, such as Kherson State University. In conclusion, it is argued that AI can significantly enhance the effectiveness of learning through adaptive trajectories, real-time feedback, dialogue analytics, and outcome prediction, provided that a human-centered approach is followed in its implementation.

ABSTRACT. This paper explores the development of diagnostic models within intelligent educational systems, with a focus on mathematical disciplines. A comparative analysis of discrete and continuous knowledge tracing approaches is presented, highlighting their strengths in adapting to students’ cognitive profiles and uncovering latent skill dynamics. In parallel, the paper introduces a novel sense-economic model that aims to enhance student motivation by recognizing meaningful contributions to learning. Educational progress is measured not solely through correctness but through deeper indicators such as analytical reasoning, conceptual understanding, creative problem-solving, and peer support. A central component of this approach is the implementation of a unified blockchain-based token (SenseCoin), which operates within a decentralized infrastructure of validated educational values—termed "senses." These tokens are awarded based on transparent, configurable criteria and can be exchanged for intrinsically valuable educational opportunities, such as access to premium learning resources, project-based modules, or mentorship-driven activities. The model supports a dynamic ontology of sense types, allowing flexible expansion across domains and disciplines. The paper argues that the combination of diagnostic precision and value-sensitive tokenization offers a promising direction for the future of educational systems—one grounded in personal meaning, autonomy, and integrity rather than coercion or formal compliance.

ABSTRACT. This article presents the experience of developing a model for constructing an individual learning trajectory for learning the English language. The model is implemented using Notation3 (N3) and N3 Logic Rules. It takes into account the student's initial knowledge level, university curricula, elements of non-formal education, and external courses from MOOC platforms such as Udemy and Coursera. The system dynamically generates personalized learning paths by aligning educational content with ontological representations and semantic reasoning.

ABSTRACT. Training deep learning models on large-scale datasets is often constrained by computational cost, time, and energy requirements. Coreset selection offers a promising solution by constructing compact and representative subsets of data that preserve the essential characteristics of the full dataset. In this paper, we propose a novel coreset selection method based on class-wise Neural Principal Component Analysis (NPCA). The method identifies dominant components for each class and selects samples that most strongly express these components. We evaluate the approach on a subset of the ImageNet dataset and compare it against established coreset selection methods, Herding and K-Center. Experimental results demonstrate that the NPCA-based method performs competitively, especially in low-data regimes, while also offering improved interpretability through visual analysis of selected samples. Our findings suggest that the proposed method is a viable and efficient strategy for training under resource constraints.

ABSTRACT. This paper presents a novel approach for generating synthetic license plate (LP) images using Latent Diffusion Models (LDMs). The generation of synthetic data is crucial in the domain of LP recognition (LPR) due to strict privacy regulations limiting access to real-world datasets. The study leverages a dataset of 250,000 Ukrainian LP images to train a LDM conditioned on LP text. To that end, multiple Variational Autoencoders (VAEs) are compared in terms of their performance, and the resulting LDM is evaluated based on visual realism and control accuracy. Results demonstrate that the LDM outperforms traditional generative models, such as GANs, in producing realistic and diverse LP images while maintaining high control over the generation process. More precisely, our model yields Fréchet Inception Distance of 16.15, compared to 31.73 obtained by the baseline. The proposed method shows promise in enhancing the quality of LPR systems while mitigating data scarcity issues.

ABSTRACT. The article is devoted to the development of a web application for automated creation of test tasks for students training in engineering sciences using com-puter vision and machine learning technologies. The proposed approach is based on the analysis of images containing text, formulas, schemas and graphs, with the subsequent generation of questions using artificial intelli-gence (AI) tools and a conceptual-thesis model. The web application, created on the basis of the low-code platform FlutterFlow, includes three functional modules for processing text data, mathematical formulas and figures, which allows adapting test tasks to educational goals and student knowledge levels. The paper explores the possibilities of integrating AI to reduce tutor work routine, increase the accuracy of educational content analysis and personal-ize learning. Obtained results have the potential to optimize pedagogical ac-tivities and further improve adaptive educational technologies.

ABSTRACT. Higher education institutions operating in hybrid warfare environments or located in high-risk regions play a critical role in safeguarding educational, scientific, and governmental stability. Based on the wartime experience of Ukrainian universities, this research proposes a university resilience architecture designed to maintain educational and administrative operations despite infrastructure damage, electricity blackouts, and cyber threats. In this study, an extensive analysis of international standards relevant to enhancing the resilience of universities was conducted, leading to the development of a comprehensive resilience architecture. The proposed model includes geo-replicated data centers, cloud-based backup infrastructures, redundant and satellite communication channels, autonomous energy systems, cyber-resilience measures, and caching server centers to support online education platforms. Future steps of this research may include integration with Digital Twin technologies, cooperation with international and governmental cyber-resilience organizations, and strengthening measures aligned with global security and resilience standards.

ABSTRACT. The paper examines approaches to organizing the educational process in Ukrainian higher education institutions during martial law, emphasizing the integration of modern information and communication technologies with a pedagogical focus. Virtual learning environments and learning management systems have become essential for maintaining educational continuity despite unprecedented challenges. The impact of war on students’ general conditions and learning experiences has been analyzed, revealing that these digital platforms offer flexibility and accessibility for both students and faculty. They enable academic activities to continue despite physical displacement and disruptions. However, the rapid transition to online learning has exposed complexities in integrating educational software, particularly the need for students and academic staff to develop the necessary digital skills. Digital literacy gaps and technical support challenges have been identified as significant barriers to the full utilization of these platforms. Addressing these issues requires targeted measures to enhance digital competencies, ensuring participants can effectively navigate virtual learning environments.

Based on the experience of Kherson State University, several initiatives have been proposed to improve digital literacy and ensure the effective use of educational platforms. Establishing dedicated digitalization assistant positions, implementing comprehensive training programs, and providing continuous technical support are key measures to facilitate this transition. The integration of artificial intelligence assistants can further enhance accessibility by offering guidance when human support is unavailable. Findings suggest that a combination of technological infrastructure and pedagogical support is crucial for the long-term resilience of higher education in Ukraine. Continued investment in digital skills development will help mitigate future disruptions, ensuring that higher education institutions can maintain stability and adaptability in crisis conditions.