A modular system structure in which modules for ontology preparation, student profile creation, rule checking, and meaningful course selection operate in a chain is presented, finding that the integration of logical rules and semantic analysis ensures rigorous verification of prerequisites and schedules while maintaining the flexibility of searching for suitable courses.
Abstract
The increasing diversity of elective courses in curricula amid the digital transformation of universities exacerbates the problem of creating individual educational plans, as students face information overload and difficulty adhering to academic rules. Conventional statistically based recommender systems do not explain their decisions and ignore prerequisites and schedules. The goal of our paper is to design the architecture of an intelligent decision support system for course selection based on an ontological approach. In this paper, we presented a modular system structure in which modules for ontology preparation, student profile creation, rule checking, and meaningful course selection operate in a chain. We demonstrated that the ontological approach enables clear definition of disciplines, professional skills, and educational requirements. We found that the integration of logical rules and semantic analysis ensures rigorous verification of prerequisites and schedules while maintaining the flexibility of searching for suitable courses. We also demonstrated that the proposed architecture solves the problem of understanding by generating a clear explanation for each recommendation using educational language. The implementation of the proposed architecture will reduce the workload of teachers and improve the quality of independent course selection by students.
The expansion of access to Digital Information and Communication Technologies and the offer of distance or semi-distance education courses that make use of virtual learning environments brought changes in the teaching and learning processes, requiring that the student be even more protagonist in this process. The present study aimed to identify important aspects to be considered in the implementation and improvement of self-paced learning and e-learning in higher education courses, with the purpose of rethinking pedagogical models of courses offered at a distance so that they reach even more of your learning objectives. The research is characterized as qualitative, of bibliographic nature, and discusses techniques to monitor and record, electronically and automatically, the results of the process and learning. The importance of processes that store and manage the student's profile is highlighted, both in terms of content and forms of access. The article proposes the use of ontologies to store information about the educational process and presents a computational architecture for this purpose.
J. Morais, Arlindo F. da Conceição, Cacilda Encarnação Augusto Alvarenga et al.· 0 citations
This article presents an intelligent system designed for objective and effective assessment of student assignments in the educational process. The proposed approach has the function of automatic processing and text extraction of documents in PDF and DOCX formats, and evaluates the semantic similarity between assignment conditions and student responses through deep learning models based on the Sentence Transformer architecture. In addition to semantic proximity metrics, the system also analyzes multidimensional linguistic features such as content coverage, internal consistency, and structural connectivity. A probabilistic classification mechanism is implemented to determine the probability of content generated by artificial intelligence. A decision-making system based on fuzzy logic theory is used to combine all assessment components into a single final score, which ensures interpretability and transparency of assessments. The result of this research makes a practical contribution to the field of automated educational technologies and creates a reliable basis for supporting decisions in large-scale educational environments.
Jasur Davletov, G. Toirova, Mukhriddin Fayziyev et al.· International Conference on...· 0 citations
Pilot test results indicate adequate average response times, thematic and temporal diversity of authors and robust data structuring, confirming its viability as an applicable prototype and advancing the integration of generative AI into library services.
Manuel Blázquez-Ochando, J. Prieto-Gutiérrez, María-Antonia Ovalle-Perandones· Library hi tech· 0 citations
The increasing acceptance of digital technologies within higher educational institutions has transformed the administration and accessibility of academic resources. Despite the availability of digital library platforms, many existing systems provide only basic search functionalities and lack intelligent mechanisms capable of recommending relevant learning materials to students. Consequently, students often experience challenges in locating suitable academic resources, resulting in information overload and inefficient utilization of available library collections. This study presents the design and implementation of a Student Library Management System using the Random Forest technique for automated academic resource recommendation. The technique accepted in this study incorporates system analysis and design principles together with machine learning techniques. The implementation process involved data collection and preprocessing, feature extraction and selection, Random Forest model training, testing, validation, and integration of the recommendation engine into the web-based library management platform. Experimental evaluation of the developed system was performed; the model attained an accuracy of 90%, precision of 92%, recall of 90%, F1-score of 90%, and AUC-ROC of 94%, indicating the reliability of the technique. These findings demonstrate that the Random Forest algorithm provides reliable recommendation performance and improves accessibility to academic resources within digital library environments.
Unknown authors· Scientific Journal of Engine...· 0 citations
Aim.
To present a set of requirements helping to visualise AI results for monitoring the compliance of educational materials with regulatory requirements, ensuring their effective integration into the practical activities of a teacher.
Methodology.
The research is based on the analysis of regulatory legal documents, as well as on the theoretical understanding of approaches and principles outlined in works devoted to Explainable artificial intelligence (XAI), the digital educational environment, and pedagogical design. The key method was system analysis, which made it possible to synthesise requirements for the interface of the educational content verification system from various sources.
Results.
A set of requirements for displaying the results of educational materials verification has been formulated and substantiated, including: a multi-window interface for simultaneously displaying the lesson plan, identified inconsistencies, regulatory references, and AI recommendations, high-precision rendering of mathematical expressions, transparent visualization of AI (XAI) logic to enhance teacher trust, integrated symbolic and spatial representation of educational content, cognitive load management through visual analytics, and engagement monitoring features and risk management indicators.
Research implications.
The theoretical significance of the work lies in the systematisation and adaptation of XAI requirements and provisions of national standards for the design of educational products with AI algorithms to the task of constructing user interfaces for educational systems using AI technologies. The practical significance consists in the development of a set of specific parameters (resolution, pixel density, colour space, etc.) that can be used in the creation and selection of hardware and software tools for the implementation of intelligent systems in the educational process, increasing their transparency and , the level of trust on the part of teachers as a consequence.
Conclusions.
The level of compliance of educational materials developed on the basis of artificial intelligence systems and the effectiveness of their application in the educational process are determined not only by the quality of their algorithms, but also by the thoughtfulness of the user interface. The work formulates and substantiates a set of requirements for the visualisation of the results of such systems, integrating the provisions of Explainable AI, national standards for the use of AI in education, and modern principles of pedagogical design. This set can serve as a regulatory basis for the design of interfaces of educational AI systems.
V. Belyaev, Yu. Obydenkov, A. Rulev· Moscow Pedagogical Journal· 0 citations
Higher education institutions globally are being mandated to implement Outcome-Based Education (OBE), placing curriculum mapping at the center of quality assurance. Yet mapping remains labour-intensive, subjective, and inadequate at scale. This paper proposes the Semantic-AI Curriculum Mapping (SACM) Framework—a conceptual architecture integrating ontologies, knowledge graphs, semantic reasoning, and Large Language Models (LLMs) to automate OBE-aligned curriculum mapping. A purposive synthesis of 30 peer-reviewed publications (2021–2026) is used to derive a six-category problem taxonomy and a six-layer framework, validated through a traceability matrix. No implementation is presented. Three original contributions are advanced: a problem taxonomy, the six-layer SACM framework, and a traceability matrix demonstrating comprehensive coverage of identified barriers. The framework's modular design is contextualized for Nigerian universities under NUC's Core Curriculum and Minimum Academic Standards (CCMAS, 2022).
Oluwatosin Amoke Fabiyi, O. Awodele, Modupe Ruth Omofoye et al.· Global Journal of Engineerin...· 0 citations