Journals / Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi / 2026 / Cilt: 7 Sayı: Özel Sayı Özel Sayı

Evolution of Simulation Technologies: AI-Enhanced Education

Simülasyon Teknolojilerinin Evrimi: Yapay Zeka ile Güçlendirilmiş Eğitim

Fulya Torun(Aydın Adnan Menderes University, Faculty of Education, Department of Computer Education and Instructional Technology)0000-0001-6942-888X
Seda Özer Şanal(Fırat University, Faculty of Economics and Administrative Sciences, Department of Management Information Systems)0000-0002-6260-9212
Pages
267–292
Publication Date
2025-11-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this context, this article, which examines the relevant challenges from a broad perspective, offers multidimensional recommendations for education policymakers, practitioners, software developers, and researchers; it emphasizes the need for future research to focus on different learning styles, long-term learning outcomes, and social inequalities.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article examines the historical development of simulation technologies and the transformation in artificial intelligence-enhanced education applications.
📊
Key Findings & Data:Currently, new generation technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR), gamification, and digital twins have been integrated into simulations.
💡
Conclusion & Impact:In this context, this article, which examines the relevant challenges from a broad perspective, offers multidimensional recommendations for education policymakers, practitioners, software developers, and researchers; it emphasizes the need for future research to focus on different learning styles, long-term learning outcomes, and social inequalities.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:YZ destekli eğitim · Simülasyon teknolojileiri · Dijital dönüşüm

Abstract

This article examines the historical development of simulation technologies and the transformation in artificial intelligence-enhanced education applications. At the outset of the historical process of simulation use, analog-based systems were primarily employed, typically finding application in fields such as military and engineering. Subsequently, with the advancement of digital technologies, simulations began to be used as an effective teaching tool in education. Currently, new generation technologies such as augmented reality (AR), virtual reality (VR), mixed reality (MR), gamification, and digital twins have been integrated into simulations. This has made learners' learning processes more interactive, personalizable, and secure. The integration of AI into these systems has created structures that offer learners individual feedback, track their performance, and adapt their learning experiences to their needs. These technologies are widely and actively used, especially in high-risk or application-based fields such as medicine, engineering, aviation, psychology, and military education. A review of the literature reveals that the contributions made in terms of effectiveness and efficiency are quite significant. However, there are various challenges in terms of access to and dissemination of these systems. In particular, infrastructure deficiencies, teachers' technological proficiency levels, ethical and legal responsibilities, data security, and access inequalities necessitate careful management of this process. AI enhanced simulations have great potential in terms of improving the quality of education and offering personalized learning experiences, clearly demonstrating that overcoming these challenges will have significant implications. In this context, this article, which examines the relevant challenges from a broad perspective, offers multidimensional recommendations for education policymakers, practitioners, software developers, and researchers; it emphasizes the need for future research to focus on different learning styles, long-term learning outcomes, and social inequalities.

Özet

Bu makalede simülasyon teknolojilerinin tarihsel gelişim süreçleri ile birlikte yapay zeka (YZ) destekli eğitim uygulamalarındaki dönüşüm ele alınmıştır. Simülasyonların kullanımına yönelik tarihsel sürecin başlangıcında öncelikle analog temelli sistemler kullanılmış olup, genellikle askeri ve mühendislik gibi alanlarda uygulama yeri bulmuştur. Ardından dijital teknolojilerin gelişmesiyle birlikte eğitim alanında da etkili bir öğretim aracı olarak kullanımına başlanılmıştır. Mevcutta ise simülasyonlara artırılmış gerçeklik (AR), sanal gerçeklik (VR), karma gerçeklik (MR), oyunlaştırma ve dijital ikiz gibi yeni nesil teknolojiler entegre edilmiştir. Böylelikle öğrenenlerin öğrenme süreçleri daha etkileşimli, kişiselleştirilebilir ve güvenli hale gelmiştir. YZ'nın bu sistemlere entegrasyonu, öğrenenlere bireysel geribildirim imkanı sunan, performanslarını takip eden ve öğrenme deneyimlerini ihtiyaçlarına göre uyarlayabilen yapılar oluşturmuştur. Bu teknolojiler özellikle tıp, mühendislik, havacılık, psikoloji ve askeri eğitim gibi yüksek riskli ya da uygulama temelli alanlarda yaygın ve aktif olarak kullanılmaktadır. Alanyazın incelendiğinde etkililik ve verimlilik bağlamında sağlanan katkıların oldukça önemli olduğu görülmektedir. Ancak ne var ki, bu sistemlere erişim ve yaygınlaştırma bağlamında çeşitli zorluklar mevcuttur. Özellikle altyapı eksiklikleri, öğretmenlerin teknolojik yeterlilik düzeyleri, etik ve yasal sorumluluklar, veri güvenliği ve erişim eşitsizlikleri göz önünde bulundurularak bu sürecin dikkatle yönetilmesini zorunlu kılmaktadır. YZ destekli simülasyonlar, eğitimin kalitesini artırma ve kişiselleştirilmiş öğrenme deneyimleri sunma açısından büyük bir potansiyele sahip olması, bu zorlukların aşılması halinde sağlanacak olan katkısının önemli yansımalarının olacağı öngörüsünü net biçimde ortaya koymaktadır. Bu kapsamda ilgili zorlukların üstesinden gelinebilmesi için geniş bir perspektifle inceleme yapılan bu makalede eğitim politikaları, uygulayıcılar, yazılım geliştiricileri ve araştırmacılar için çok boyutlu öneriler sunulmuş olup; gelecekteki araştırmalar için farklı öğrenme biçimleri, uzun vadeli öğrenme çıktıları ve toplumsal eşitsizliklere odaklanılması gerekliliği vurgulanmıştır.

🔖

Cite This Article

Torun, F., Şanal, S. Ö. (2025). Evolution of Simulation Technologies: AI-Enhanced Education. Necmettin Erbakan Üniversitesi Ereğli Eğitim Fakültesi Dergisi, Cilt: 7 Sayı: Özel Sayı Özel Sayı, 267–292. https://ajindex.com/articles/1532059

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (129)

  1. Ajluni, V. (2025). Artificial intelligence in psychiatric education: Enhancing clinical competence through simulation. Industrial Psychiatry Journal, 34(1), 11-15. https://10.4103/ipj.ipj_377_24
  2. Akavova, A., Zarema, T., & Zarina, L. (2023). Adaptive learning and artificial intelligence in the educational space. E3S Web of Conferences, 451, 06011. https://doi.org/10.1051/e3sconf/202345106011
  3. Alam, A. (2023). Leveraging the power of ‘modeling and computer simulation’ for education: An exploration of its potential for improved learning outcomes and enhanced student engagement. In 2023 International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) (pp. 1–6). IEEE. https://doi.org/10.1109/DICCT56244.2023.10110159
  4. Ali, M. (2025). The role of AI in reshaping medical education: opportunities and challenges. The Clinical Teacher, 22(2), e70040. https://doi.org/10.1111/tct.70040
  5. Aliabadi, R. (2023). The impact of an artificial intelligence (AI) project-based learning (PBL) course on middle-school students’ interest, knowledge, and career aspiration in the AI field [Master’s thesis, Robert Morris University].
  6. Alnoukari, M., Shafaamry, M., Aytouni, K., & Damascus, S. (2013). Simulation for computer sciences education. Communications of the ACS, 6(1), 1-18.
  7. Andreenkov, E., & Shunaev, S. (2022). Application of simulation modeling as a replacement for laboratory practice in engineering education. In 2022 VI International Conference on Information Technologies in Engineering Education (Inforino) (pp. 1–6). IEEE. https://doi.org/10.1109/Inforino53888.2022.9782940
  8. Bai, Z., & Jin, L. (2015, November). Study on application of computer simulation technology in physical education. In 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering (pp. 850-854). Atlantis Press. https://doi.org/10.2991/ICCMCEE-15.2015.156
  9. Bala, M. M., Akkineni, H., Sirivella, S. A., Ambati, S., & Potharaju Venkata Sai, K. V. (2023). Implementation of an adaptive E-learning platform with facial emotion recognition. Microsystem Technologies, 29(4), 609-619. https://doi.org/10.1007/s00542-023-05420-1
  10. Barberousse, A., & Vorms, M. (2013). Computer simulations and empirical data. In Duran, J. M. & Arnold, E. (Eds). Computer simulations and the changing face of scientific experimentation (pp. 29-45). Cambridge Scholars Publishing.
  11. Barlow, M., & Rowlands, E. (2012). Quantification of game AI performance for junior leadership training in the defence domain. In Handbook of Research on Serious Games as Educational, Business and Research Tools (pp. 1097-1121). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-4666-0149-9.CH057
  12. Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1(3), 311-320.
  13. Billings, D. R. (2012). Efficacy of adaptive feedback strategies in simulation-based training. Military Psychology, 24(2), 114–133. https://doi.org/10.1080/08995605.2012.672905
  14. Blum, M. (1972). Analog simulation of an AC automobile generator. Simulation, 19(4), 140 - 144. https://doi.org/10.1177/003754977201900407
  15. Bonde, L. (2024). A Framework for integrating emerging technologies into technical and vocational education and training. Africa Journal of Technical and Vocational Education and Training, 9(1), 97-107. https://doi.org/10.69641/afritvet.2024.91184
  16. Brigas, C. J. (2019). Modeling and simulation in an educational context: Teaching and learning sciences. Research in Social Sciences and Technology, 4(2), 1–12. https://doi.org/10.46303/ressat.04.02.1
  17. Camargo, C., et al. (2021). Systematic literature review of realistic simulators applied in educational robotics context. Sensors, 21(12), 4031. https://doi.org/10.3390/s21124031
  18. Cano-Parra, R., Gomez-Sanchez, E., Bote-Lorenzo, M. L., & González-Martínez, J. A. (2013, November). Cloud-based simulation for education: an illustrative scenario. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (pp. 209-214). https://doi.org/10.1145/2536536.2536568
  19. Carlson, C. (2023). Virtual and augmented simulations in mental health. Current Psychiatry Reports, 2023(25), 365-371. https://doi.org/10.1007/s11920-023-01438-4
  20. Chan, C., Zheng, Q., Xu, C., Wang, Q., & Heng, P. A. (2024, June). Adaptive federated learning for EEG emotion recognition. In 2024 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  21. Chang, Q., et al. (2022). Artificial intelligence technologies for teaching and learning in higher education. International Journal of Reliability, Quality and Safety Engineering, 29(05), 2240006. https://doi.org/10.1142/S021853932240006X
  22. Chen, X., Xie, H., & Hwang, G. J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, 100005. https://doi.org/10.1016/j.caeai.2020.100005
  23. Cheng, B., Zhang, Y., & Shi, D. (2018). Ontology-based personalized learning path recommendation for course learning. In 2018 9th International Conference on Information Technology in Medicine and Education (ITME) (pp. 1–5). IEEE. https://doi.org/10.1109/ITME.2018.00123
  24. Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499–541. https://doi.org/10.3102/0034654320933544
  25. Chiniara, G., & Crelinsten, L. (2019). A brief history of clinical simulation: how did we get here?. Clinical Simulation (pp. 3-16). Academic Press. https://doi.org/10.1016/b978-0-12-815657-5.00001-2
  26. Cisse, A. H. (2024, November). Real-time Adaptive learning environments using gaze and emotion recognition engagement and learning outcomes. In International Conference on Computers in Education.
  27. Clark, D. (2020). Artificial intelligence for learning: How to use AI to support employee development. Kogan Page Publishers.
  28. D’Angelo, C., Rutstein, D., Harris, C., Haertel, G., Bernard, R., & Borokhovski, E. (2013). Review of computer-based simulations for STEM learning in K-12 education. Menlo Park, CA: SRI International.
  29. Delva, I., Lytvynenko, N., Delva, M., Pinchuk, V., & Kryvchun, A. (2019). Simulation in medical education: history of the development. Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії, 19(2), 183-185. https://doi.org/10.31718/2077-1096.19.2.183
  30. Demir, M., et al. (2023). Adaptive artificial intelligence to teach interactive molecular dynamics in the context of human-computer interaction. bioRxiv. https://doi.org/10.1101/2023.08.26.554965
  31. Deshpande, A., & Samuel, H. (2011). Simulation games in engineering education: A state‐of‐the‐art review. Computer Applications in Engineering Education, 19(3), 399–410. https://doi.org/10.1002/cae.20323
  32. Diaz-Guio, D. A., Henao, J., Pantoja, A., Arango, M. A., Díaz-Gómez, A. S., & Gómez, A. C. (2024). Artificial intelligence, applications and challenges in simulation-based education. Colombian Journal of Anestesiology, 52(1). https://doi.org/10.5554/22562087.e1085
  33. Dillenbourg, P. (2016). The evolution of research on digital education. International Journal of Artificial Intelligence in Education, 26, 544–560. https://doi.org/10.1007/s40593-016-0106-z
  34. Esquembre, F., Martin-Blas, T., Bayo, A., & Martin, M. (2019). Easy Java/JavaScript simulations as a tool for learning analytics. arXiv. https://doi.org/10.48550/arXiv.1910.09156
  35. Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230-235.
  36. Francès, G., Siebers, P.-O., & Aickelin, U. (2015). Decision making in agent-based models. In M. Dastani, G. A. Kaminka, & M. Lomuscio (Eds.), Multi-Agent Systems: 12th European Conference, EUMAS 2014, Prague, Czech Republic, December 18–19, 2014, Revised Selected Papers (pp. 379–393). Springer. https://doi.org/10.1007/978-3-319-17130-2_25
  37. Ghani, U. (2014). Effect of feedback mechanisms on students' learning in the use of simulation-based training in a computer engineering program. QScience Proceedings, 2015(4), 59. https://doi.org/10.5339/QPROC.2015.ELC2014.59
  38. Goecks, V. G., Waytowich, N., Asher, D. E., Park, S. J., Mittrick, M., Richardson, J.,. & Kott, A. (2023). On games and simulators as a platform for development of artificial intelligence for command and control. The Journal of Defense Modeling and Simulation, 20(4), 495–508. https://doi.org/10.1177/15485129221083278
  39. Grabusts, P. (2016, May). Possibilities of simulation models visualization in teaching process. In Society. Integration. Education: Proceedings of the International Scientific Conference (Vol. 2, pp. 527–534).
  40. Greenhalgh, T. M., & Dijkstra, P. (2024). How to Read a Paper: The Basics of Evidence-based Healthcare. John Wiley & Sons.
  41. Gu, X., & Blackmore, K. L. (2015). A systematic review of agent-based modelling and simulation applications in the higher education domain. Higher Education Research and Development, 34(5), 883–898. https://doi.org/10.1080/07294360.2015.1011088
  42. Hamilton, A. (2024). Artificial intelligence and healthcare simulation: the shifting landscape of medical education. Cureus, 16(5). https://10.7759/cureus.59747
  43. Harman, H. (1961). Simulation: a survey. IRE-AIEE-ACM Computer Conference. 1-9. https://doi.org/10.1145/1460690.1460692
  44. Herur-Raman, A., Almeida, N. D., Greenleaf, W., Williams, D., Karshenas, A., & Sherman, J. H. (2021). Next-generation simulation—integrating extended reality technology into medical education. Frontiers in Virtual Reality, 2, 693399. https://doi.org/10.3389/frvir.2021.693399
  45. Hiltz, F. (1962). Analog computer simulation of a neural element. Ire Transactions on Bio-medical Electronics, 9(1), 12-20. https://doi.org/10.1109/TBMEL.1962.4322944
  46. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
  47. Hrastinski, S., Olofsson, A. D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G.,. & Utterberg, M. (2019). Critical imaginaries and reflections on artificial intelligence and robots in postdigital K-12 education. Postdigital Science and Education, 1, 427–445. https://doi.org/10.1007/s42438-019-00046-x
  48. Humphreys, P. (2019). Computer Simulations. Philosophical Papers. https://doi.org/10.1093/oso/9780199334872.003.0002
  49. Jiao, Y., Zhang, J., Yang, X., Zhan, T., Wu, Z., Li, Y.,. & Cao, Y. (2023). Artificial intelligence–assisted evaluation of the spatial relationship between brain arteriovenous malformations and the corticospinal tract to predict postsurgical motor defects. American Journal of Neuroradiology, 44(1), 17–25. https://doi.org/10.3174/ajnr.A7735
  50. Jung, S. (2023). Challenges for future directions for artificial intelligence integrated nursing simulation education. Korean Journal of Women Health Nursing, 29(3), 239-242. https://doi.org/10.4069/kjwhn.2023.09.06.1
  51. Kabalan, K. Y., El-Hajj, A., & Wazz, N. (1991). Graphical simulation of an analog computer. International Journal of Electrical Engineering Education, 28(4), 341-349. https://doi.org/10.1177/002072099102800
  52. Kang, J., Chen, Z., & Kang, W. (2024, August). Virtual reality technology and algorithm application in intelligent combat training simulation system. In 2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC) (pp. 787-791). IEEE. https://doi.org/10.1109/PEEEC63877.2024.00147
  53. Kannan, J., & Munday, P. (2018). New trends in second language learning and teaching through the lens of ICT, networked learning, and artificial intelligence. In C. Fernández Juncal & N. Hernández Muñoz (Eds.), Vías de transformación en la enseñanza de lenguas con mediación tecnológica. Círculo de Lingüística Aplicada a la Comunicación, 76, 13–30. http://dx.doi.org/10.5209/CLAC.62495
  54. Komasawa, N. (2024). Transformative Landscape of Anesthesia Education: Simulation, AI Integration, and Learner-Centric Reforms: A Narrative Review. Anesthesia Research, 1(1), 34-43. https://doi.org/10.7759/cureus.40940
  55. Kumar, K. S., Tamil Selvan, D. M., Kalaiyarasan, G., Ramnath, R., & Kumar, N. S. (2023). Examining the role of virtual reality, augmented reality, and artificial intelligence in adapting stem education for next-generation inclusion. International Journal of Emerging Knowledge Studies, 2(12), 876-883. https://doi.org/10.70333/ijeks-02-12-025
  56. Lasic-Lazic, J., Pavlina, K., & Pongrac, A. (2011, May). Software simulation as educational tool. In 2011 Proceedings of the 34th International Convention MIPRO (pp. 1160-1162). IEEE.
  57. Lebo, C., & Brown, N. (2024). Integrating artificial intelligence (AI) simulations into undergraduate nursing education: an evolving AI patient. Nursing Education Perspectives, 45(1), 55-56. https://doi.org/1 0.1097/01.NEP.0000000000001081
  58. Lee, H. (2024). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Sciences Education, 17(5), 926–931. https://doi.org/10.1002/ase.2270
  59. Lee, M., Kim, H., Choi, H., & Song, H. (2022). Acceleration of applying AI to open intelligent network using parallel simulation for RL training. In 2022 IEEE Globecom Workshops (GC Wkshps) (pp. 1026–1031). IEEE. https://doi.org/10.1109/GCWkshps56602.2022.10008682
  60. Leemkuil, H. H., de Jong, T., & Ootes, S. A. (2000). Review of educational use of games and simulations. https://ris.utwente.nl/ws/portalfiles/portal/5156063/review_of_educational.pdf
  61. Li, D., Yang, Z., Tang, S., Zhao, H., & Zhang, X. (2022). A mirror environment to produce artificial intelligence training data. IEEE Access, 10, 24578–24586. https://doi.org/10.1109/ACCESS.2022.3154825
  62. Li, H., Ke, N., Zhang, A., & Huang, X. (2024). Unraveling the motivational tapestry of AI-driven gamification in education. International Journal of Global Perspectives in Academic Research, 1(3). https://doi.org/10.70339/znd1nk22
  63. Liao, T. T. (1972). The use of analog computer simulation for learning modeling concepts and skills. Journal of Educational Technology Systems, 1(2), 135-153. https://doi.org/10.2190/0J70-J21P-60HT-3PPF
  64. Lim, E. M. (2024). Metaphor analysis on pre-service early childhood teachers’ conception of AI (Artificial Intelligence) education for young children. Thinking Skills and Creativity, 51, 101455. https://doi.org/10.1016/j.tsc.2024.101455
  65. Luckin, R. (2018). Machine Learning and Human Intelligence. The future of education for the 21st century. UCL institute of education press.
  66. Mallam, S., Nazir, S., & Renganayagalu, S. (2019). Rethinking maritime education, training, and operations in the digital era: Applications for emerging immersive technologies. Journal of Marine Science and Engineering, 7(12), 428. https://doi.org/10.3390/jmse7120428
  67. Mallik, S., & Gangopadhyay, A. (2023). Proactive and reactive engagement of artificial intelligence methods for education: A review. Frontiers in Artificial Intelligence, 6, 1151391. https://doi.org/10.3389/frai.2023.1151391
  68. Mariani, A. W., & Pego-Fernandes, P. M. (2011). Medical education: simulation and virtual reality. Sao Paulo Medical Journal, 129(6), 369-370. https://doi.org/10.1590/S1516-31802011000600001
  69. Marinkovic, M., Cavoski, S., & Markovic, A. (2014). Application of cloud-based simulation in scientific research. In Handbook of Research on High Performance and Cloud Computing in Scientific Research and Education (pp. 281-307). IGI Global.
  70. McCarlie, P., & Hunter, A. (2021). Using Game AI to Control a Simulated Economy. In ICAART (2) (pp. 629-634). https://doi.org/10.5220/0010212306290634
  71. McLeod, J., & McLeod, S. (1982). Simulation in the Service of Society. Simulation, 39, ix - xii. https://doi.org/10.1177/003754978203900609
  72. Meclea, M-A., Goga, A. S., & Boșcoianu, M. (2024). Aspects regarding artificial intelligence use in military and engineering sciences aircraft propulsion. Scientific Research and Education in the Air Force. https://doi.org/10.19062/2247-3173.2024.25.7
  73. Mello, R. F., Freitas, E., Pereira, F. D., Cabral, L., Tedesco, P., & Ramalho, G. (2023). Education in the age of generative AI: Context and recent developments. arXiv. https://doi.org/10.48550/arXiv.2309.12332
  74. Nafea, I. T. (2018). Machine learning in educational technology. In F. Karray & H. M. Abbas (Eds.), Machine learning – Advanced techniques and emerging applications (pp. 175–183). IntechOpen. https://doi.org/10.5772/intechopen.72906
  75. Nay, J. J., & Gill, J. M. (2015). Data-driven dynamic decision models. In 2015 Winter Simulation Conference (WSC) (pp. 3728–3739). IEEE. https://doi.org/10.1109/WSC.2015.7408381
  76. Norling, E., Sonenberg, L., & Rönnquist, R. (2000). Enhancing multi-agent based simulation with human-like decision making strategies. In J. S. Sichman, R. Conte, & N. Gilbert (Eds.), Multi-Agent Systems and Agent-Based Simulation: International Workshop, MABS 2000 Proceedings (pp. 206–223). Springer. https://doi.org/10.1007/3-540-44561-7_16
  77. Overstreet, C. M., & Martens, A. (2006). Introduction to special issue: Modeling and simulation in teaching and training. Simulation, 82(11), 681–683. https://doi.org/10.1177/0037549707077059
  78. Park, J. J., Tiefenbach, J., & Demetriades, A. K. (2022). The role of artificial intelligence in surgical simulation. Frontiers in Medical Technology, 4, 1076755. https://doi.org/10.3389/fmedt.2022.1076755
  79. Peisachovich, E. H., Da Silva, C., Maier, C., & Mccutcheon, K. (2019). Proposing a model to embed a simulated-person methodology program within higher education. Innovations in Education and Teaching International, 56(1), 46–56. https://doi.org/10.1080/14703297.2017.1399808
  80. Peng, Y., Ahmad, S. F., Ahmad, A. Y. B., Al Shaikh, M. S., Daoud, M. K., & Alhamdi, F. M. H. (2023). Riding the waves of artificial intelligence in advancing accounting and its implications for sustainable development goals. Sustainability, 15(19), 14165. https://doi.org/10.3390/su151914165
  81. Philbrick, G. (1963). Analogs Yesterday, Today, and Tomorrow. Simulation, 1(1), 11 - 17. https://doi.org/10.1177/003754976300100103
  82. Pokrivčáková, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education.
  83. Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., Britten, N., Roen, K., & Duffy, S. (2006). Guidance on the conduct of narrative synthesis in systematic reviews. A product from the ESRC methods programme Version, 1(1), b92.
  84. Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12, 22. https://doi.org/10.1186/s41039-017-0062-8
  85. Prakosa, I., Nugroho, S., & Wulandari, D. (2024). Ship evacuation simulation based on reinforcement learning: a case study on NPCs behavior. 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA), 226-231. https://doi.org/10.1109/ISITIA63062.2024.10667736
  86. Psotka, J. (2013). Modeling, simulations and education. Interactive Learning Environments, 21(4), 319–320. https://doi.org/10.1080/10494820.2013.808880
  87. Qadir, J. (2023). Engineering education in the era of ChatGPT: Promise and pitfalls of generative AI for education. In 2023 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–5). IEEE. https://doi.org/10.1109/EDUCON54358.2023.10125121
  88. Qian, Y. (2017). Computer simulation in higher education: Affordances, opportunities, and outcomes. In Handbook of research on innovative pedagogies and technologies for online learning in higher education (pp. 236-262). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-5225-1851-8.CH011
  89. Ranchhod, A., Gurău, C., Lace, J., & Brunsdon, C. (2014). Evaluating the educational effectiveness of simulation games: A value generation model. Information Sciences, 264, 75–90. https://doi.org/10.1016/j.ins.2013.09.008
  90. Raybaut, A. (2020). Analog computing simulations and the production of theoretical evidence in economic dynamics. Œconomia. History, Methodology, Philosophy, 10(2), 309-329. https://doi.org/10.4000/oeconomia.8421
  91. Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. F. (2024). Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Education and Information Technologies, 29(1), 273–295. https://doi.org/10.1007/s10639-023-12250-1
  92. Schiff, D. (2021). Out of the laboratory and into the classroom: The future of artificial intelligence in education. AI & Society, 36(1), 331–348. https://doi.org/10.1007/s00146-020-01033-8
  93. Semrl, N., Papac, V., Noventa, M., & Simunic, V. (2023). AI language models in human reproduction research: Exploring ChatGPT’s potential to assist academic writing. Human Reproduction, 38(12), 2281–2288. https://doi.org/10.1093/humrep/dead207
  94. Sharrab, Y., Almutiri, N., Tarawneh, M., Alzyoud, F., Al-Ghuwairi, A., & Al-Fraihat, D. (2023). Toward smart and immersive classroom based on AI, VR, and 6G. Int. J. Emerg. Technol. Learn. 18(2), 4-16. https://doi.org/10.3991/ijet.v18i02.35997
  95. Siregar, I., Albar, A. R., Subhan, M., Siregar, M. A., & Purnama, Y. (2023). Optimizing the use of simulation technology in digital learning content development. Al-Hijr: Journal International Inspire Education Technology, 2(2), 107–112. https://doi.org/10.55849/jiiet.v2i2.460
  96. Sottilare, R. A. (2024). Examining the role of knowledge management in adaptive military training systems. In International Conference on Human-Computer Interaction (pp. 300-313). Cham: Springer Nature Switzerland.
  97. Suttor, J., Camilleri, F., Mikic, F., & Talas, D. (2019). Implement AI service into VR training. In Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning (pp. 82–86). https://doi.org/10.1145/3372806.3374909
  98. Swan, B. A., Giordano, N. A., Febres-Cordero, S., Fugate, K., & Steiger, L. (2025). Integrating artificial intelligence technology into simulation for pre-and postlicensure nursing students. Nursing Education Perspectives, https://doi.org/10-1097. 10.1097/01.NEP.0000000000001397
  99. Sziegat, H. (2024). Virtual simulation games in entrepreneurship education: status quo and prospects. European Conference on Games Based Learning,18(1). https://doi.org/10.34190/ecgbl.18.1.3001
  100. Tafazoli, D., & Gómez Parra, M. E. (2017). Robot-assisted language learning: Artificial intelligence in second language acquisition. In M. R. Mahalik & M. Khosrow-Pour (Eds.), Intelligent Computational Systems: A Multi-Disciplinary Perspective (pp. 370–396). Bentham Science Publishers.
  101. Tolk, A. (2018, July). Simulation and modeling as the essence of computational science. In SummerSim (pp. 8-1).
  102. Topçu, O. (2014). Adaptive decision making in agent-based simulation. Simulation, 90(7), 815–832. https://doi.org/10.1177/0037549714536930
  103. Trenholme, R. (1994). Analog simulation. Philosophy of Science, 61(1), 115 - 131. https://doi.org/10.1086/289783.
  104. Troyan, P., Bertulfo, T. F., & Kamp, F. (2020). Postpartum hemorrhage: A novel approach to large classroom simulation and debriefing. Clinical Simulation in Nursing, 48, 59-63. https://doi.org/10.1016/j.ecns.2020.08.008
  105. Tselegkaridis, S., & Sapounidis, T. (2021). Simulators in educational robotics: A review. Education Sciences, 11(1), 1–17. https://doi.org/10.3390/educsci11010011
  106. Uğur, S., & Kuş, G. (2024). Design and implementation of interactive virtual reality supported first aid training. Interactive Learning Environments, 1–12. https://doi.org/10.1080/10494820.2024.2350641
  107. van Lent, M., Carpenter, P., McAlinden, R., & Tan, P. G. (2004). A Tactical and Strategic AI Interface for Real-Time Strategy Games. In Challenges in Game Artificial Intelligence–Papers from the AAAI Workshop Technical Report WS-04-04.
  108. Verawati, N. N. S. P., & Nisrina, N. (2024). The role of artificial intelligence (AI) in transforming physics education: A narrative review. Lensa: Jurnal Kependidikan Fisika, 12(2), 212- 228. https://doi.org/10.33394/j-lkf.v12i2.13523
  109. Waheed, H., Hassan, S. U., Aljohani, N. R., Hardman, J., & Alelyani, S. (2020). Predicting academic performance of students from VLE big data using deep learning models. Computers in Human Behavior, 104, 106189. https://doi.org/10.1016/j.chb.2019.106189
  110. Wan, H., Zhang, T., & Zhang, X. (2023). Learning path recommendation based on knowledge tracing and reinforcement learning. In 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 50–54). IEEE. https://doi.org/10.1109/ICALT58122.2023.00021
  111. Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
  112. Wartman, S. A., & Combs, C. D. (2018). Medical education must move from the information age to the age of artificial intelligence. Academic Medicine, 93(8), 1107–1109.
  113. Wieman, C. E., Adams, W. K., & Perkins, K. K. (2008). PhET: Simulations that enhance learning. Science, 322(5902), 682–683. https://doi.org/10.1126/science.1161948
  114. Wilkins, C., & Odell, P. (1998). Using computer simulation for analyzing educational strategies. Mathematical and Computer Modelling, 27, 31-42. https://doi.org/10.1016/S0895-7177(97)00252-5
  115. Wu, Q., He, Y., Huang, L., Liu, Y., Wang, X., Li, M., & Huang, G. (2022). Virtual simulation in undergraduate medical education: A scoping review of recent practice. Frontiers in Medicine, 9, 855403. https://doi.org/10.3389/fmed.2022.855403
  116. Xiong, X., Wang, S., & Wang, B. (2024). Self-play decision-making method of deep reinforcement learning guided by behavior tree under complex environment. In 2024 43rd Chinese Control Conference (CCC) (pp. 3988–3993). IEEE. https://doi.org/10.23919/CCC63176.2024.10662399
  117. Xu, S., & Zhang, X. (2023). Augmenting human cognition with an AI-mediated intelligent visual feedback. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–14). https://doi.org/10.1145/3544548.3580905
  118. Yılmaz, A. A. (2025). From simulators to skies: engineering and educational advancements in pilot training: a bibliometric perspective. Black Sea Journal of Engineering and Science, 8(2), 35-36. https://doi.org/10.34248/bsengineering.1629319
  119. Yuan, H., & Van Gool, R. C. (2021). Presim: A 3D photo-realistic environment simulator for visual AI. IEEE Robotics and Automation Letters, 6(2), 2501–2508. https://doi.org/10.1109/LRA.2021.3061994
  120. Yuan, K. C., Tsai, L. W., Lee, K. H., Cheng, Y. W., Hsu, S. C., Lo, Y. S., & Chen, R. J. (2020). The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit. International Journal of Medical Informatics, 141, 104176. https://doi.org/10.1016/j.ijmedinf.2020.104176
  121. Yue, M., Jong, M. S. Y., & Ng, D. T. K. (2024). Understanding K–12 teachers’ technological pedagogical content knowledge readiness and attitudes toward artificial intelligence education. Education and Information Technologies, 1–32. https://doi.org/10.1007/s10639-024-12621-2
  122. Zekaj, R. (2023). AI language models as educational allies: Enhancing instructional support in higher education. International Journal of Learning, Teaching and Educational Research, 22(8), 120–134. https://doi.org/10.26803/ijlter.22.8.7
  123. Zheng, Y., Ding, J., Liu, F., & Wang, D. (2023). Adaptive neural decision tree for EEG based emotion recognition. Information Sciences, 643, 119160. https://doi.org/10.1016/j.ins.2023.119160
  124. Zhou, G. (2024). Navigating the future landscape of gamified education. In SHS Web of Conferences, 187. https://doi.org/10.1051/shsconf/202418702005
  125. Zhu,Y. (2025). Revolutionizing simulation-based clinical training with AI: Integrating FASSLING for enhanced emotional intelligence and therapeutic competency in clinical psychology education. Journal of Clinical Technology and Theory, 2, 38-54. https://doi.org/10.54254/3049-5458/2025.21247
  126. Ziakkas, D., Kim, G. B. M., & Synodinou, D. E. (2024). Virtual reality (VR) and simulated air traffic control environment (satce) in flight training: the purdue case study. Intelligent Human Systems Integration (IHSI 2024): Integrating People and Intelligent Systems, 119(119). https://doi.org/10.54941/ahfe1004565
  127. Goecks, V. G., Waytowich, N., Asher, D. E., Park, S. J., Mittrick, M., Richardson, J.,. & Kott, A. (2023). On games and simulators as a platform for development of artificial intelligence for command and control. The Journal of Defense Modeling and Simulation, 20(4), 495–508. https://doi.org/10.1177/15485129221083278
  128. Hrastinski, S., Olofsson, A. D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G.,. & Utterberg, M. (2019). Critical imaginaries and reflections on artificial intelligence and robots in postdigital K-12 education. Postdigital Science and Education, 1, 427–445. https://doi.org/10.1007/s42438-019-00046-x
  129. Jiao, Y., Zhang, J., Yang, X., Zhan, T., Wu, Z., Li, Y.,. & Cao, Y. (2023). Artificial intelligence–assisted evaluation of the spatial relationship between brain arteriovenous malformations and the corticospinal tract to predict postsurgical motor defects. American Journal of Neuroradiology, 44(1), 17–25. https://doi.org/10.3174/ajnr.A7735