Electronic modeling

Vol 48, No 1 (2026)

CONTENTS

Mathematical modeling and Computation Methods

 

HAVRYSH V.I.
Mathematical Models and Analysis of Temperature Regime in a Plate with a Foreign Thermocreative Element


3-21
 

ZALUZHNYI V.F., LYSETSYI Y.M.
Theoretical and Methodological Aspects of Building a System for Managing the Armed Forces


22-32

Informational Technologics

 

KHYDYNTSEV M.M., KHOMENKO O.A.
Information Technologies in Cyber Insurance


33-50
 

TARANOWSKI A.O., SAMOYLOV V.D.
Creation of Text-Based Training Materials for Personnel Training in Nuclear Power Enterprises Using Artificial Intelligence


51-70
 

DRAHUNTSOV R., ZUBOK V.
Methods for Maintaining the Observability of Information and Communication Infrastructure in Conditions of Full-Scale War


71-86
  KOMAROV M., ZAIKA N., MARTINYK I.
Artificial Intelligence Technologies as a Tool for Ensuring the Cybersecurity of Critical Information Infrastructure
 
87-98

Parallel Computing

 

TSYRUL O.
Prospects for Managing Database Record Versions


99-105

Application of Modeling Methods and Facilities

 

SKRUPSKYI S.YU., TIAHUNOVA M.YU., BOROVYK O.V.
Approach to Solving the Problem of Resource Allocation in the Implementation of Critical Logistics Scenarios


106-123

MATHEMATICAL MODELS AND ANALYSIS OF TEMPERATURE REGIME IN A PLATE WITH A FOREIGN THERMOCREATIVE ELEMENT

V.I. Havrysh

Èlektron. model. 2026, 48(1):03-21

https://doi.org/10.15407/emodel.48.01.003

ABSTRACT

Linear and nonlinear mathematical models of the heat conduction process for an isotropic plate with a foreign semi-through inclusion in which internal heat sources are uniformly concentrated have been developed. For this purpose, the thermophysical parameters of the inhomogeneous plate are described using asymmetric unit functions. As a result, linear and nonlinear heat conduction equations with discontinuous and singular coefficients have been obtained. A linearizing function has been introduced to the nonlinear differential equation and boundary conditions and a quasi-linear boundary value problem has been obtained. For its complete linearization, the temperature has been approximated as a function of spatial coordinates on the inclusion surfaces and the plate boundary surface. This made it possible to obtain a linear boundary value problem with respect to the linearizing function. To solve the boundary value problems of thermal conductivity, the integral Fourier transform was used and analytical and analytical-numerical solutions were obtained in the form of improper convergent integrals. On this basis, an algorithm and software tools were developed that made it possible to obtain the temperature distribution in spatial coordinates and analyze the heat transfer processes in the given structure.

KEYWORDS

thermal conductivity of the material; temperature field; isotropic plate; semi-through foreign inclusion; thermally insulated surface; ideal thermal contact, thermal sensitivity of the material, convective heat transfer.

REFERENCES

  1. Ying C., Man L., & Yongjie H. (2020). Emerging Interface Materials for electronics thermal management: experiments, modeling, and new opportunities. Journal of Materials Chemistry. Vol. 8, P. 10568-10586.
    https://doi.org/10.1039/C9TC05415D
  2. Nattadon, P., Phadungsak, R., Snunkhaem, E., Suwipong, H., & Kriengkrai, N. (2020). The investigation of heat absorber on the efficiency of slanted double-slope solar distillation unit. International Journal of Heat and Technology, Vol. 38, no 1, P. 171-179. 
    https://doi.org/10.18280/ijht.380119
  3. Zhang, Z., Sun, Y., Cao, X., Xu, J., & Yao, L. (2024). A slice model for thermoelastic analysis of porous functionally graded material sandwich beams with temperature-dependent material properties. Thin-Walled Structures, Vol. 198, 111700. 
    https://doi.org/10.1016/j.tws.2024.111700
  4. Zhang, Z., Zhou, D., Fang, H., Zhang, J., & Li, X. (2021). Analysis of layered rectangular plates under thermo-mechanical loads considering temperature-dependent material properties. Applied Mathematical Modelling, Vol. 92, P. 244-260. 
    https://doi.org/10.1016/j.apm.2020.10.036
  5. Filimonenko, N.M., & Filimonenko, K.V. (2020). Analysis of capability for improvement of basic mathematical model of electrode of ferro-alloy furnace. Bulletin of the Volodymyr Dahl Eastern Ukrainian national university, № 7 (263), P. 53-57. 
    https://doi.org/10.33216/1998-7927-2020-263-7-53-57
  6. Maher, A., Sadiq, Al-B., Zainab, M., Noor, A., Alan, B., & Dongsheng, W. (2020). CFD analysis of a nanofluid-based microchannel heat sink. Thermal Science and Engineering Progress. Vol. 20, 100685. 
    https://doi.org/10.1016/j.tsep.2020.100685
  7. Junwei, L., Ying, Z., Debao, Z., Shifei, J., Zhuofen, Z., & Zhihua, Z. (2020). Model development and performance evaluation of thermoelectric generator with radiative cooling heat sink. Energy Conversion and Management. Vol. 216, 112923. 
    https://doi.org/10.1016/j.enconman.2020.112923
  8. Hu, S., Li, C., & Zhou, Z. et al. (2023). Nanoparticle-enhanced coolants in machining: mechanism, application, and prospects. Frontiers of Mechanical Engineering. Vol. 18, P. 53.
    https://doi.org/10.1007/s11465-023-0769-8
  9. Xingwen, P., Xingchen, L., Zhiqiang, G., Xiaoyu, Z., & Wen, Y. (2022). A deep learning method based on partition modeling for reconstructing temperature field. International Journal of Thermal Sciences. Vol.18 2, 107802. 
    https://doi.org/10.1016/j.ijthermalsci.2022.107802
  10. Ren, Y., Huo, R., Zhou, D., & Zhang, Z. (2023). Thermo—mechanical buckling analysis of restrained columns under longitudinal steady-state heat conduction. Iranian Journal of Science and Technology — Transactions of Civil Engineering. Vol. 47, issue 3, P. 1411-1423. 
    https://doi.org/10.1007/s40996-022-01020-7
  11. Yongcun, Z., Siqi, W., Yuheng, L., Pengli, Z., Feixiang, W., Feng, L., Vignesh, M., Williams, W., Amit, N., Zhe, W., & Zhanhu, G. (2020). Recent advances in thermal interface materials. ES Materials & Manufacturing. Vol. 7, P. 4-24.
    https://doi.org/10.30919/esmm5f717
  12. Yu-Ming, C., Faisal, S., Ijaz, K., Seifedine, K., Zahra, A., & Waqar, A. (2020). Cattaneo-christov double diffusions (CCDD) in entropy optimized magnetized second grade nanofluid with variable thermal conductivity and mass diffusivity. Journal of Materials Research and Technology. 9(6), P. 13977-13987.
    https://doi.org/10.1016/j.jmrt.2020.09.101
  13. Gael, S., Atsuki, K., Jacques, J., Gildas, C., & Laurent, L. (2020). Regenerative cooling using elastocaloric rubber: analytical model and experiments. Journal of Applied Physics, 127 (9), 094903.
    https://doi.org/10.1063/1.5132361
  14. Haoran, L., Jiaqi, Y., & Ruzhu, W. (2023). Dynamic compact thermal models for skin temperature prediction of portable electronic devices based on convolution and fitting methods. International Journal of Heat and Mass Transfer. Vol. 210, 124170. 
    https://doi.org/10.1016/j.ijheatmasstransfer.2023.124170
  15. Ghannad, M., & Yaghoobi, M. (2015). A thermoelasticity solution for thick cylinders subjected to thermo-mechanical loads under various boundary conditions. International Journal of Advanced Design & Manufacturing Technology. Vol. 8, no 4, P. 1-12.
  16. Havrysh, V., Dzhumelia, E., Kachan, S., Serdyuk, P., & Maikher, V. (2024). Constructing mathematical models of thermal conductivity in individual elements and units of electronic devices at local heating considering thermosensitivity. Eastern-European Journal of Enterprise Technologies, 3(5 (129)), P. 25-35. 
    https://doi.org/10.15587/1729-4061.2024.304804
  17. Havrysh, V., Dzhumelia, E., Kachan, S., Maikher, V., & Rabiichuk, I. (2024). Construction of mathematical models of thermal conductivity for modern electronic devices with elements of a layered structure. Eastern-European Journal of Enterprise Technologies, 4(5(130)), P. 34-44. 
    https://doi.org/10.15587/1729-4061.2024.309346
  18. Havrysh, V., Dzhumelia, E., Hrytsai, O., Kachan, S., & Maikher, V. (2024). Development of mathematical models of heat conductivity for modern electronic devices with elements containing foreign inclusions. Eastern-European Journal of Enterprise Technologies, 5(5(131)), P. 70-79.
    https://doi.org/10.15587/1729-4061.2024.313747

Full text: PDF

 

THEORETICAL AND METHODOLOGICAL ASPECTS OF BUILDING A SYSTEM FOR MANAGING THE ARMED FORCES

V.F. Zaluzhnyi, Y.M. Lysetsyi

Èlektron. model. 2026, 48(1):22-32

https://doi.org/10.15407/emodel.48.01.022

ABSTRACT

The article considers theoretical and methodological aspects of building a system for managing armed forces as an open organizational system operating within the framework of a situational approach. It is shown that the management system is based on information support for the processes of developing and implementing decisions, a set of standard procedures for solving the tasks set, a system for activating personnel, and consists of subsystems of methodology, structure, process, and management techniques. A functional diagram of the management system and its cybernetic model are presented, which represent a formalization of the relationships between the management subject, the management object, and connections with the external environment. It is determined that the main purpose of the armed forces management system is to develop and implement management decisions to shape the necessary behavior of the management object in conditions of various environmental influences and to achieve the stated goals of ensuring military security and armed protection of the sovereignty, independence, and territorial integrity of the state.

KEYWORDS

open system, organizational system, armed forces, management, structure, functions, model, process.

REFERENCES

  1. Hatsenko S.S. (2015). Analiz vymoh do system upravlinnia viiskamy ta shliakhy yikh udoskonalennia. Zbirnyk naukovykh prats Tsentru voienno-stratehichnykh doslidzhen NUOU im. Cherniakhivskoho, no. 3, pp. 85-90.
  2. Zaluzhnyi V.F. (2025). Systema upravlinnia viiskamy ta zbroieiu: tendentsii rozvytku v umovakh suchasnoi zbroinoi borotby. Elektronne modeliuvannia, vol. 47, no. 2, pp. 67-80.
  3. Ustymenko O.V. Osoblyvosti vprovadzhennia v ZS Ukrainy system viiskovoho upravlinnia za pryntsypamy ta standartamy NATO. Zbirnyk naukovykh prats Tsentru voienno-stratehichnykh doslidzhen NUOU im. Cherniakhivskoho, no. 3, pp. 18-27.
  4. Korolenko V.A., Siniavskyi V.K., Vereshchahin S.I. (2018). Avtomatizatsiia sistemy upravleniia voiskami: na puti ot idei k resheniiu. Avtomatizatsiia upravleniia voiskami, 2013, no. 1, pp. 32-39.
  5. Snitiuk V.E. (2015). Evoliutsionnye tekhnologii priniatiia reshenii v usloviiakh neopredelennosti: monografiia. Kyiv: MP Lesia, 347 p.
  6. Lysetskyi Yu.M.(2018). Informatsiini tekhnolohii v upravlinni ta obrobtsi informatsii: monografiia. Kyiv: LAT&K, 268 p.
  7. Svydruk I.I., Myronov Yu.B., Kundytskyi O.O. (2013). Teoriia orhanizatsii: pidruchnyk. Lviv: Novyi Svit-2000, 175 p.
  8. Hrytsiuk P.M., Dzhoshi O.I., Hladka O.M. (2021). Osnovy teorii system i upravlinnia: navchalnyi posibnyk. Rivne: NUVHP, 272 p.
  9. Setrov M.I. (1972). Osnovy funktsionalnoi teorii organizatsii. Leningrad: Nauka, 163 p.
  10. Lysetskyi Yu.M. (2014). Model i systema upravleniia predpriiatiem. In: Informatsiini ta modeliuiuchi tekhnolohii: materialy Vseukr. nauk.-prakt. konf. (Cherkasy, 29-31 travnia 2014 r.). Cherkasy, 2014, pp. 55-57.
  11. Koontz, H., OʼDonnell, C. (1976). A systems and contingency analysis of managerial functions. Business & Economics, 824 p.
  12. Tesler H.S. (2004). Novaia kibernetika: monografiia. Kyiv: Lohos, 404 p.

Full text: PDF

 

INFORMATION TECHNOLOGIES IN CYBER INSURANCE

M.M. Khydyntsev, O.A. Khomenko

Èlektron. model. 2026, 48(1):33-50

https://doi.org/10.15407/emodel.48.01.033

ABSTRACT

This paper is devoted to reviewing and analyzing the application of information technologies in cyber insurance business processes. Cyber insurance processes are considered in accordance with the requirements of industry regulations and international standards. The aim of the study is to determine the directions and level of implementation of modern information technologies in the financial and insurance sector of Ukraine. The methods of comparative analysis, generalization, and modeling are used. The main cyber insurance processes that provide for or ensure the possibility of applying the latest information technologies are identified, and the level of individual technologies implementation into the best global practices of insurance and cyber insurance is researched using the example of blockchain technology, semantic methods, artificial intelligence, and machine learning technologies, information security, and cybersecurity. Some cyber insurance processes are common with insurance processes, the automation of which is carried out using similar approaches and algorithms. A three-component, five-level model for the implementation of information technologies in cyber insurance in Ukraine is proposed, and recommendations and stages for the implementation of this model are identified. A study of the application of individual elements of the proposed model by leading insurance companies in Ukraine is proposed. The results can be used to improve the effectiveness of the digital transformation of cyber insurance processes and the formation of cyber risk insurance policies.

KEYWORDS

cyber insurance, information technologies, business processes, cyber insurance models, artificial intelligence, modeling, financial and insurance sector.

REFERENCES

  1. Lander & Rogers (March 2025). CyberSight 360: Cyber insurance trends shaping 2025 and beyond. https://www.landers.com.au/legal-insights-news/cyber-insurance-trends-shaping-2025-and-beyond
  2. Huntress (February 4, 2025). Buckman, B. The 2025 Cyber Insurance Trends Report. https://www.huntress.com/blog/cyber-insurance-trends
  3. S&P Global Ratings (November 27, 2024). Adam, M., Emura, K., Ashworth, S., Polizu, C., Kolli, S. Cyber Insurance Market Outlook 2025: Cycle Management Will Be Key to Sustaining Profits. https://www.spglobal.com/ratings/en/research/articles/241127-cyber-insurance-market-outlook-2025-cycle-management-will-be-key-to-sustaining-profits-13323968
  4. Ramsky, A.Yu., Arabadzhi, K.V. (2023). Cyberinsurance in the banking sector: risk identification and security support tools. Scientific Bulletin of the International Association of Scientists. Series: economics, management, security, technologies, 2(2), 1-12. 
    https://doi.org/10.56197/2786-5827/2023-2-2-3
  5. UkrNDNT (2023). Information security management. Guidelines for cyberinsurance. (DSTU ISO/IEC 27102:2023). https://zakon.rada.gov.ua/rada/show/v0135774-23#Text
  6. Board of the National Bank of Ukraine (2025). Resolution "On approval of the Regulations on the organization of measures to ensure information security and cyber protection by financial service providers" (draft). https://bank.gov.ua/admin_uploads/article/proekt_ 2025-03-10.pdf
  7. International Organization for Standardization. (2019). Information security management Guidelines for cyber-insurance (International Standard ISO/IEC 27102:2019(E)).
  8. Khudyntsev, M.M., Zhylin, A.V. & Davydiuk, A.V. (2021). World Cybersecurity Indices: Overview and Formation Methods (Global Report / Catalog) International Cybersecurity University, Institute of Modeling Problems in Energy named after G.E. Pukhov NAS of Ukraine, Feniks.
  9. World Bank Group (2016). GIIF Achievements in ACP Countries: Global Index Insurance Facility. Phase 1 (20102015). https://documents1.worldbank.org/curated/en/ 482761490702615329/pdf/113713-WP-ENGLISH-GIIF-ACP-Report-Eng-Web-PUBLIC.pdf
  10. N-iX (2025). Voloshynsky, Yu. Top 6 main technological trends in the insurance industry for 2024. https://our-thinking.nashtechglobal.com/insights/new-technology-in-the-insurance- industry.
  11. Vivolo-Kantor, A.M., Martell, B.N., Holland, K.M., & Westby, R.P. (2014). A systematic review and content analysis of bullying and cyber-bullying measurement strategies. Aggression and violent behavior, 19(4), 423-434.
    https://doi.org/10.1016/j.avb.2014.06.008
  12. Romanosky, S., Ablon, L., Kuehn, A., & Jones, T. (2019). Content analysis of cyber insurance policies: how do carriers price cyber risk? J. Cybersecurity, 5(1), tyz002.
    https://doi.org/10.1093/cybsec/tyz002
  13. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805.
    https://doi.org/10.1016/j.comnet.2010.05.010
  14. European Union Agency for Cybersecurity (ENISA) (February 23, 2023). Demand Side of Cyber Insurance in the EU: Analysis of Challenges and Perspectives of OESs. https://www.enisa.europa.eu/publications/demand-side-of-cyber-insurance-in-the-eu#contentList
  15. Munich Re (April 26, 2024). Global Cyber Risk and Insurance Survey 2024. https://www.munichre.com/en/insights/cyber/global-cyber-risk-and-insurance-survey.html
  16. Biener, C., Eling, M., & Wirfs, J.H. (2015). Insurability of cyber risk: An empirical analysis. The Geneva Papers on Risk and Insurance-Issues and Practice, 40(1), 131-158. 
    https://doi.org/10.1057/gpp.2014.19
  17. Ellili, N., Nobanee, H., Alsaiari, L., Shanti, H., Hillebrand, B., Hassanain, N., Elfout, L. (2023). The applications of Big Data in the insurance industry: A bibliometric and systematic review of relevant literature. The Journal of Finance and Data Science, 9(100102), 1-27.
    https://doi.org/10.1016/j.jfds.2023.100102
  18. International Association of Insurance Supervisors (IAIS) (February 2020). Issues Paper on the Use of Big Data Analytics in Insurance. https://www.iais.org/uploads/2022/01/200319-Issues-Paper-on-Use-of-Big-Data-Analytics-in-Insurance-FINAL.pdf
  19. European Union Agency for Cybersecurity (ENISA) (February 2024). Cyber insurance models and methods and the use of AI, ENISA Research and Innovation Brief. https://www.enisa.europa.eu/sites/default/files/publications/ENISA%20Research%20and% 20Innovation%20-%20AI%20and%20Cyber%20Insurance.pdf
  20. Algarni, A.M., Thayananthan, V., Malaiya, Y.K. (2021). Quantitative Assessment of Cybersecurity Risks for Mitigating Data Breaches in Business Systems. Applied Sciences, 11(8), app11083678.
    https://doi.org/10.3390/app11083678
  21. Calin, R., Badea, L., Scheau, M., Gabudeanu, L., Panait, I. & Radu, V. (2024). Cyber insurance risk analysis framework considerations. The Journal of Risk Finance, 25(2), 224-252.
    https://doi.org/10.1108/JRF-10-2023-0245
  22. Society of Actuaries Research Institute (September 2022). Dubois, E.V., Keskin, O.F. Tatar, U. Cyber Risk Modeling Methods and Data Sets. A Systematic Interdisciplinary Literature Review for Actuaries. https://www.soa.org/4a81c2/globalassets/assets/files/resources/research-report/2022/cyber-risk-modeling.pdf (accessed 15.10.2025).
  23. Rios Insua, D., Baylon, C., & Vila, J. (2021). Security risk models for cyber insurance. Unidel University Press. 
    https://doi.org/10.1201/9780429329487
  24. Help Net Security (June 25, 2025). Hill, J. Stella Cyber updates MITRE ATT&CK Aligned Coverage Analyzer. https://www.helpnetsecurity.com/2025/06/25/stellar-cyber-mitre-attck-aligned-coverage-analyzer/
  25. Awiszus, K., Penner, I., Svindland, G., Voß, A. & Weber, S. (2023). Modeling and Pricing Cyber Insurance — Idiosyncratic, Systematic, and Systemic Risks. European Actuarial Journal, 13, 1-53.
    https://doi.org/10.1007/s13385-023-00341-9
  26. National Institute of Standards and Technology (February 26, 2024). Pascoe, C., Quinn, S., & Scarfone, K. The NIST Cybersecurity Framework (CSF) 2.0 (NIST CSWP 29)
    https://doi.org/10.6028/NIST.CSWP.29
  27. Farao, A., Paparis, G., Panda, S., Panaousis, E.A., Zarras, A., & Xenakis, C. (2023). INCHAIN: a cyber insurance architecture with smart contracts and self-sovereign identity on top of blockchain. International Journal of Information Security, 1-25. 
    https://doi.org/10.1007/s10207-023-00741-8
  28. Kumar, S., Loo, L., Kocian, L. (2024). Blockchain Applications in Cyber Liability Insurance. International Journal on Cybernetics & Informatics (IJCI), 13(5), 119-139. 
    https://doi.org/10.5121/ijci.2024.130508
  29. Fortune Business Insights (September 29, 2025). Cyber Insurance Market Size, Share & Industry Trends Analysis, By Insurance Type (Standalone and Tailored), By Coverage Type (First-party and Liability Coverage), By Enterprise Size (SMEs and Large Enterprise), By End-user (Healthcare, Retail, BFSI, IT & Telecom, Manufacturing, and Others), and Regional Forecast, 2024-2032. https://www.fortunebusinessinsights.com/cyber-insurance-market-106287
  30. European Union Agency for Cybersecurity (ENISA) (November 7, 2016). Cyber insurance: Recent advances, good practices and challenges. https://www.enisa.europa.eu/publications/cyber-insurance-recent-advances-good-practices-and-challenges (accessed 15.10.2025).
  31. Khudyntsev, M., Khomenko, O. (2025). Automation of standardized cyber insurance processes. Environmental Safety and Natural Resources, 54(2), 143-153.
    https://doi.org/10.32347/2411-4049.2025.2.143-153

Full text: PDF

 

CREATION OF TEXT-BASED TRAINING MATERIALS FOR PERSONNEL TRAINING IN NUCLEAR POWER ENTERPRISES USING ARTIFICIAL INTELLIGENCE

A.O. Taranowski, V.D. Samoylov

Èlektron. model. 2026, 48(1):51-73

https://doi.org/10.15407/emodel.48.01.051

ABSTRACT

The possibility of using generative artificial intelligence technologies based on large language models to create training materials for personnel training in the energy sector, using nuclear power enterprises as an example, has been researched. The relevance of the issue is determined by the significant volume of regulatory and technical documentation, as well as the high resource intensity of the traditional process of preparing training materials. The regulatory requirements for personnel training, the current state of automated text summarization technologies, and the risks associated with artificial intelligence hallucinations were analyzed. An approach to automating the creation of the learning component of knowledge assessment courses is proposed by structuring, extractive and abstractive summarization of regulatory documents, as well as processing graphic materials included in such documents. An experimental test was conducted using common generative artificial intelligence tools, and the results were compared with materials created by experts in the subject area. The results show that comparable accuracy and clarity were achieved without the negative impact of hallucinations and with a significant reduction in development time, provided that expert control by humans is maintained. We concluded that the proposed approach is practical and promising for improving the effectiveness of training personnel at energy companies.

KEYWORDS

artificial intelligence, large language models, generative artificial intelligence, knowledge assessment, learning materials.

REFERENCES

  1. JSC “NNEGC “Energoatom”. (2025). List of the operating organisation’s normative documents in force
  2. SE“NNEGC “Energoatom”. (2021). Personal qualification management. Training of personnel of SE“NNEGC “Energoatom”. Terms and definitions (101:2021)
  3. SE “NNEGC “Energoatom”. (2022). Personal qualification management. Requirements for learning and methodological materials (102:2022)
  4. The financing agreement for the annual nuclear safety action programme 2010 — Part II, International agreement between the Government of Ukraine and the European Union (2011). https://zakon.rada.gov.ua/laws/show/994_a69
  5. SE “NNEGC “Energoatom”. (2021). Regulation of distance learning of personnel of SE “NNEGC “Energoatom” (PL-D.0.07.698-21)
  6. SE “NNEGC “Energoatom”. (2021). Personal qualification management. Requirements for technical training aids for NPP Branch personnel (244:2021)
  7. Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8(10), 1839–1850. 
    https://doi.org/10.1038/s41562-024-02004-5
  8. Merine, R., & Purkayastha, S. (2022). Risks and Benefits of AI-generated Text Summarization for Expert Level Content in Graduate Health Informatics. In 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). IEEE. 
    https://doi.org/10.1109/ICHI54592.2022.00113
  9. Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology. 
    https://doi.org/10.1111/bjet.13370
  10. Leiker, D., Finnigan, S., Ricker Gyllen, A., & Cukurova, M. (2023). Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale. In Proceedings of the workshop on empowering education with llms - the next-gen interface and content generation 2023 co-located with 24th international conference on artificial intelligence in education (AIED 2023). AIED Society. https://ceur-ws.org/Vol-3487/short1.pdf
  11. Murray, T. (1999). Authoring Intelligent Tutoring Systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education, 10(1), 98-129. https://telearn.hal.science/hal-00197339v1
  12. Mikeladze, T. (2023). Creating teaching materials with ChatGPT. Proceedings of the IRCEELT–2023 13th International Research Conference on Education. International Black Sea University School of Education, Humanities, and Social Sciences. https://ircelt.ibsu.edu.ge/wp-content/uploads/2023/09/A4-PROCEDINGS-BOOK-IRCEELT-2023.pdf
  13. Yang, S.J.H., Ogata, H., Matsui, T., & Chen, N.-S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008.
    https://doi.org/10.1016/j.caeai.2021.100008
  14. Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P. S., Yang, Q., & Xie, X. (2024). A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology.
    https://doi.org/10.1145/3641289
  15. Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics.
    https://doi.org/10.18653/v1/2020.acl-main.173
  16. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., & Fung, P. (2022). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys
    https://doi.org/10.1145/3571730
  17. Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for Customizable Learning Experiences. Sustainability, 16(7), 3034. 
    https://doi.org/10.3390/su16073034
  18. ASKO. (no date). Computer based personnel training courses ASOT. https://aspect.asot.com.ua/asko
  19. Development of databases for educational courses for the Automated computer-based learning and knowledge assessment system “ASKO”. (2020, December 4). Public Procurements of Ukraine — Prozorro. https://prozorro.gov.ua/uk/tender/UA-2020-12-04-002753-a
  20. Development of databases for occupational safety and special safety regulations (NPAOP) training courses for the Automated computer-based learning and knowledge assessment “ASKN”. (2020, October 9). Public Procurements of Ukraine — Prozorro. https://prozorro.gov.ua/uk/tender/UA-2020-10-09-006885-a
  21. Learn about Gemini, the everyday AI assistant from Google. (no date). Google Gemini. https://gemini.google/about
  22. ChatGPT. (no date). OpenAI. https://openai.com/chatgpt/overview
  23. Microsoft Edge Copilot. Microsoft. https://www.microsoft.com/edge/features/copilot
  24. Sharma, A., & Aggarwal, M. (2025). A Holistic Review of Image-to-Text Conversion: Techniques, Evaluation Metrics, Multilingual Captioning, Storytelling and Integration. SN Computer Science, 6(3). 
    https://doi.org/10.1007/s42979-025-03719-6
  25. Caffagni, D., Cocchi, F., Barsellotti, L., Moratelli, N., Sarto, S., Baraldi, L., Baraldi, L., Cornia, M., & Cucchiara, R. (2024). The Revolution of Multimodal Large Language Models: A Survey. In Findings of the Association for Computational Linguistics ACL 2024 (p. 13590-13618). Association for Computational Linguistics
    https://doi.org/10.18653/v1/2024.findings-acl.807
  26. ElSayed, M., Shultz, J., & Kurtz, J. (2025). User-friendly AI-driven automation for rapid building energy model generation. Energy and Buildings, 116092.
    https://doi.org/10.1016/j.enbuild.2025.116092
  27. Adnin, R., & Das, M. (2024). "I look at it as the king of knowledge": How Blind People Use and Understand Generative AI Tools. In ASSETS '24: The 26th International ACM SIGACCESS Conference on Computers and Accessibility (p. 1-14). ACM. 
    https://doi.org/10.1145/3663548.3675631
  28. Zhang, A., Zhao, E., Wang, R., Zhang, X., Wang, J., & Chen, E. (2025). Multimodal large language models for medical image diagnosis: Challenges and opportunities. Journal of Biomedical Informatics, 169, 104895. 
    https://doi.org/10.1016/j.jbi.2025.104895
  29. Fu, B., Hadid, A., & Damer, N. (2025). Generative AI in the context of assistive technologies: Trends, limitations and future directions. Image and Vision Computing, 154, 105347.
    https://doi.org/10.1016/j.imavis.2024.105347
  30. Qin, L., Chen, Q., Zhou, Y., Chen, Z., Li, Y., Liao, L., Li, M., Che, W., & Yu, P.S. (2025). A survey of multilingual large language models. Patterns, 6(1), 101118.
    https://doi.org/10.1016/j.patter.2024.101118

Full text: PDF