Simülasyon Teknolojilerinin Evrimi: Yapay Zeka ile Güçlendirilmiş Eğitim

Year 2025, Volume: 7 Issue: Özel Sayı, 267 - 292, 29.11.2025

Fulya Torun , Seda Özer Şanal

Abstract

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.

Keywords

YZ destekli eğitim , Simülasyon teknolojileiri , Dijital dönüşüm

Ethical Statement

Bu makale herhangi bir etik onay gerektirmez.

Evolution of Simulation Technologies: AI-Enhanced Education

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.

Keywords

AI enhanced education , Simulation technologies , Digital transformation

Ethical Statement

This article does not require any ethical approval.

References

• 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
• 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
• 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
• 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
• 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].
• Alnoukari, M., Shafaamry, M., Aytouni, K., & Damascus, S. (2013). Simulation for computer sciences education. Communications of the ACS, 6(1), 1-18.
• 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
• 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
• 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
• 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.
• 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
• Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1(3), 311-320.
• 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
• Blum, M. (1972). Analog simulation of an AC automobile generator. Simulation, 19(4), 140 - 144. https://doi.org/10.1177/003754977201900407
• 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
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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.
• Clark, D. (2020). Artificial intelligence for learning: How to use AI to support employee development. Kogan Page Publishers.
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230-235.
• 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
• 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
• 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
• 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).
• Greenhalgh, T. M., & Dijkstra, P. (2024). How to Read a Paper: The Basics of Evidence-based Healthcare. John Wiley & Sons.
• 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
• Hamilton, A. (2024). Artificial intelligence and healthcare simulation: the shifting landscape of medical education. Cureus, 16(5). https://10.7759/cureus.59747
• Harman, H. (1961). Simulation: a survey. IRE-AIEE-ACM Computer Conference. 1-9. https://doi.org/10.1145/1460690.1460692
• 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
• 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
• Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
• 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
• Humphreys, P. (2019). Computer Simulations. Philosophical Papers. https://doi.org/10.1093/oso/9780199334872.003.0002
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Luckin, R. (2018). Machine Learning and Human Intelligence. The future of education for the 21st century. UCL institute of education press.
• 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
• 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
• 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
• 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.
• 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
• McLeod, J., & McLeod, S. (1982). Simulation in the Service of Society. Simulation, 39, ix - xii. https://doi.org/10.1177/003754978203900609
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Philbrick, G. (1963). Analogs Yesterday, Today, and Tomorrow. Simulation, 1(1), 11 - 17. https://doi.org/10.1177/003754976300100103
• Pokrivčáková, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education.
• 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.
• 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
• 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
• Psotka, J. (2013). Modeling, simulations and education. Interactive Learning Environments, 21(4), 319–320. https://doi.org/10.1080/10494820.2013.808880
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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.
• Tolk, A. (2018, July). Simulation and modeling as the essence of computational science. In SummerSim (pp. 8-1).
• Topçu, O. (2014). Adaptive decision making in agent-based simulation. Simulation, 90(7), 815–832. https://doi.org/10.1177/0037549714536930
• Trenholme, R. (1994). Analog simulation. Philosophy of Science, 61(1), 115 - 131. https://doi.org/10.1086/289783.
• 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
• Tselegkaridis, S., & Sapounidis, T. (2021). Simulators in educational robotics: A review. Education Sciences, 11(1), 1–17. https://doi.org/10.3390/educsci11010011
• 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
• 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.
• 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
• 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
• 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
• 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
• 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.
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Zhou, G. (2024). Navigating the future landscape of gamified education. In SHS Web of Conferences, 187. https://doi.org/10.1051/shsconf/202418702005
• 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
• 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