Integrating AI into educational game design: an AI-enhanced MDA framework

  • Zhipeng Wen School of Education, University of Glasgow, Glasgow, UK
  • Samuel Kai Wah Chu School of Nursing and Health Studies, Hong Kong Metropolitan University (HKMU), Hong Kong SAR, China
DOI: https://doi.org/10.25304/rlt.v34.3746
Keywords: AI-enhanced framework, MDA framework, educational game design, game-based learning, constructivist learning

Abstract

This study proposes an artificial intelligence (AI)-enhanced framework that integrates AI with the Mechanics, Dynamics, and Aesthetics framework through theory synthesis and framework development. It explores how generative AI, adaptive learning algorithms, and procedural content generation enhance gameplay to advance educational game design. The framework aligns AI capabilities with constructivist learning principles, supporting personalized, engaging, and scalable game-based learning. While the framework offers theoretical and practical guidance for AI-integrated educational games, further research is needed to assess its empirical effectiveness across diverse learning settings.

Downloads

Download data is not yet available.

References


Alam, A., & Mohanty, A. (2023). Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10(2), 2283282. https://doi.org/10.1080/23311916.2023.2283282




Alexiou, A., & Schippers, M. C. (2018). Digital game elements, user experience and learning: A conceptual framework. Education and Information Technologies, 23, 2545–2567. https://doi.org/10.1007/s10639-018-9730-6




Anderson, J. R. et al. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207. https://doi.org/10.1207/s15327809jls0402_2




Antony, V. N., & Huang, C. M. (2025). ID. 8: Co-creating visual stories with generative AI. ACM Transactions on Interactive Intelligent Systems, 14(3), 1–29. https://doi.org/10.1145/3672277




Bae, H. et al. (2024). Pre-service teachers’ dual perspectives on generative AI: Benefits, challenges, and integration into their teaching and learning. Online Learning, 28(3), 131–156. https://doi.org/10.24059/olj.v28i3.4543




Bond, M. et al. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z




Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1), 87–110. https://doi.org/10.1023/A:1011143116306




Chamola, V. et al. (2024). Beyond reality: The pivotal role of generative AI in the metaverse. IEEE Internet of Things Magazine, 7(4), 126–135. https://doi.org/10.1109/IOTM.001.2300174




Chen, S. (2024). Literature review of application of AI in improving gaming experience: NPC behavior and dynamic difficulty adjustment.




Chiotaki, D., Poulopoulos, V., & Karpouzis, K. (2023). Adaptive game-based learning in education: A systematic review. Frontiers in Computer Science, 5, 1062350. https://doi.org/10.3389/fcomp.2023.1062350




Csikszentmihalyi, M., & Csikzentmihaly, M. (1990). Flow: The Psychology of Optimal Experience (Vol. 1990). Harper & Row, 1.




DaCosta, B. (2025, March). Dynamic narratives: Exploring language acquisition through AI-driven text-based games. In, Society for Information Technology & Teacher Education International Conference, Association for the Advancement of Computing in Education (AACE), 611–616.




Dahrén, M. (2021). The usage of PCG techniques within different game genres. Bachelor’s thesis, Malmö University. DiVA Portal. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-46422.




Esiri, S. (2022). A digital innovation model for enhancing competitive gaming engagement and user experience. International Journal of Multidisciplinary Research and Growth Evaluation, 3(1), 752–760. https://doi.org/10.54660/.IJMRGE.2022.3.1.752-760




Gkintoni, E. et al. (2025). Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. Brain Sciences, 15(2), 203. https://doi.org/10.3390/brainsci15020203




Guo, M. et al. (2023, August). Exploring the intersection of complex aesthetics and generative AI for promoting cultural creativity in rural China after the post-pandemic era. In, International Conference on AI-generated Content, Springer Nature Singapore, 313–331. https://doi.org/10.1007/978-981-99-7587-7_27




Hall, R. (2024). Generative AI and re-weaving a pedagogical horizon of social possibility. International Journal of Educational Technology in Higher Education, 21(1), 12. https://doi.org/10.1186/s41239-024-00445-6




Herrera, M., & Vargas, L. (2025). Generative AI in gaming and virtual worlds. International Journal of Advanced Research in Arts, Science, Engineering and Management, 12(2), 857–862.




Hevner, A. R. March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625




Hind, D., & Harvey, C. (2024). Using a NEAT approach with curriculums for dynamic content generation in video games. Personal and Ubiquitous Computing, 28(3), 629–641. https://doi.org/10.1007/s00779-024-01801-z




Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.




Hossan, M. M., Fouda, M. M., & Eishita, F. Z. (2024, November). Adaptive game design using machine learning techniques: A survey. In, 2024 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), IEEE, 144–150. https://doi.org/10.1109/IoTaIS64014.2024.10799330




Hunicke, R., LeBlanc, M., & Zubek, R. (2004, July). MDA: A formal approach to game design and game research. In, Proceedings of the AAAI Workshop on Challenges in Game AI (Vol. 4, No. 1), 1722. AAAI Press.




Jabareen, Y. (2009). Building a conceptual framework: Philosophy, definitions, and procedure. International Journal of Qualitative Methods, 8(4), 49–62. https://doi.org/10.1177/160940690900800406




Kas Elias, S., & Salaimeh, O. (2024). Challenging games for all: Adaptive difficulty and its impact on inclusivity in action games. Bachelor’s thesis, Blekinge Institute of Technology. DiVA Portal. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:bth:26793.




Koster, R. (2013). Theory of Fun for Game Design. O’Reilly Media, Inc.




Kruse, J. (2019). Designer-Driven Procedural Game Content Generation Using Multi-Agent Evolutionary Computation. Doctoral Dissertation, Thesis, Auckland University of Technology, Retrieved from https://openrepository.aut.ac.nz/handle/10292/12944




Lazaridis, L., & Fragulis, G. F. (2024). Creating a newer and improved Procedural Content Generation (PCG) algorithm with minimal human intervention for computer gaming development. Computers, 13(11), 304. https://doi.org/10.3390/computers13110304




Mehta, N. (2025). The role of AI in game development and player experience. Available at SSRN 5101269. https://doi.org/10.2139/ssrn.5101269




Ng, D. T. K. et al. (2024). Fostering students’ AI literacy development through educational games: AI knowledge, affective and cognitive engagement. Journal of Computer Assisted Learning, 40(5), 2049–2064. https://doi.org/10.1111/jcal.13009




Niederman, F. (2021). Project management: Openings for disruption from AI and advanced analytics. Information Technology & People, 34(6), 1570–1599. https://doi.org/10.1108/ITP-09-2020-0639




Ninaus, M., & Nebel, S. (2021, January). A systematic literature review of analytics for adaptivity within educational video games. Frontiers in Education (Vol. 5). Frontiers Media SA, 611072. https://doi.org/10.3389/feduc.2020.611072




Oliveira, C. V., & Rito, P. N. (2024, December). Enhancing player experience through generative artificial intelligence: Custom interaction in game design. In, International Conference on Videogame Sciences and Arts, Springer Nature Switzerland, 259–268. https://doi.org/10.1007/978-3-031-81713-7_18




Paraschos, P. D., & Koulouriotis, D. E. (2023). Game difficulty adaptation and experience personalization: A literature review. International Journal of Human–Computer Interaction, 39(1), 1–22. https://doi.org/10.1080/10447318.2021.2020008




Quintana, C. et al. (2018). A scaffolding design framework for software to support science inquiry. In E. A. Davis & N. Miyake (Eds.), Scaffolding, Psychology Press, 337–386. https://doi.org/10.4324/9780203764411-4




Rantanen, R. (2023). Open-source tools for automatic generation of game content. Bachelor’s thesis, University of Helsinki. HELDA.




Ratican, J., & Hutson, J. (2024a). Adaptive worlds: Generative AI in game design and future of gaming, and interactive media. ISRG Journal of Arts, Humanities and Social Sciences, 2(5), 13894497. https://doi.org/10.5281/zenodo.13894497




Ratican, J., & Hutson, J. (2024b). Video game development 3.0: AI-driven collaborative co-creation. Metaverse, 5(2), 2904. https://doi.org/10.54517/m2904




Romero-Mendez, E. A. et al. (2023). The use of deep learning to improve player engagement in a video game through a dynamic difficulty adjustment based on skills classification. Applied Sciences, 13(14), 8249. https://doi.org/10.3390/app13148249




Ryan, R. M., Rigby, C. S., & Przybylski, A. (2006). The motivational pull of video games: A self-determination theory approach. Motivation and Emotion, 30(4), 344–360. https://doi.org/10.1007/s11031-006-9051-8




Saffari, S., Dorrigiv, M., & Yaghmaee, F. (2024). Enthusiasms, challenges, and future trends in procedural content generation in games using machine learning. Modeling and Simulation in Electrical and Electronics Engineering, 3(4), 7–22.




Samson, G. (2024). Procedurally generated AI compound media for expanding audial creations, broadening immersion and perception experience. International Journal of Electronics and Telecommunications, 70(2), 149550. https://doi.org/10.24425/ijet.2024.149550




Sayed, W. S. et al. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging e-learning platform. Multimedia Tools and Applications, 82(3), 3303–3333. https://doi.org/10.1007/s11042-022-13076-8




Schell, J. (2008). The Art of Game Design: A Book of Lenses. CRC Press.




Sel, F. (2025). Non-believable agents: Representation, play, and AI in ape out. In E. Saka (Ed.), Understanding Generative AI in a Cultural Context: Artificial Myths and Human Realities, IGI Global Scientific Publishing, 109–132. https://doi.org/10.4018/979-8-3693-7235-7.ch005




Shaker, N., Togelius, J., & Nelson, M. J. (2016). Procedural Content Generation in Games. Springer International Publishing. https://doi.org/10.1007/978-3-319-42716-4




Sicart, M. (2008). Defining game mechanics. Game Studies, 8(2), 1–14.




Smith, G. (2014, April). Understanding procedural content generation: A design-centric analysis of the role of PCG in games. In, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 917–926. Association for Computing Machinery. https://doi.org/10.1145/2556288.2557341




Song, Y., Wu, K., & Ding, J. (2024). Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies–‘LearningverseVR’. Computers & Education: X Reality, 4, 100069. https://doi.org/10.1016/j.cexr.2024.100069




Summerville, A. et al. (2018). Procedural content generation via machine learning (PCGML). IEEE Transactions on Games, 10(3), 257–270. https://doi.org/10.1109/TG.2018.2846639




Swacha, J., & Gracel, M. (2025). Supporting serious game development with generative artificial intelligence: Mapping solutions to lifecycle stages. Applied Sciences, 15(21), 11606. https://doi.org/10.3390/app152111606




Sweetser, P., & Wyeth, P. (2005). GameFlow: A model for evaluating player enjoyment in games. Computers in Entertainment (CIE), 3(3), 3. https://doi.org/10.1145/1077246.1077253




Sweller, J., Van Merrienboer, J. J., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205




Tang, Y. et al. (2020). A personalized learning system for parallel intelligent education. IEEE Transactions on Computational Social Systems, 7(2), 352–361. https://doi.org/10.1109/TCSS.2020.2965198




Tanskanen, S. (2018). Player immersion in video game: Designing an immersive game project. Bachelor’s thesis, South-Eastern Finland University of Applied Sciences. Theseus. Retrieved from http://urn.fi/URN:NBN:fi:amk-201805189034.




Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356–367. https://doi.org/10.1177/1534484305278283




VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369




Webb, S., & Chang, A. C. (2015). Second language vocabulary learning through extensive reading with audio support: How do frequency and distribution of occurrence affect learning? Language Teaching Research, 19(6), 667–686. https://doi.org/10.1177/1362168814559800




Westera, W. et al. (2020). Artificial intelligence moving serious gaming: Presenting reusable game AI components. Education and Information Technologies, 25, 351–380. https://doi.org/10.1007/s10639-019-09968-2




Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing E-Learning. Morgan Kaufmann.




Yannakakis, G. N., & Togelius, J. (2018). Artificial Intelligence and Games (Vol. 2). Springer, 2475–2502. https://doi.org/10.1007/978-3-319-63519-4
Published
2026-03-17
How to Cite
Wen , Z., & Chu , S. K. W. (2026). Integrating AI into educational game design: an AI-enhanced MDA framework. Research in Learning Technology, 34. https://doi.org/10.25304/rlt.v34.3746
Issue
Vol. 34 (2026)
Section
Original Research Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to Research in Learning Technology retain the copyright of their article and at the same time agree to publish their articles under the terms of the Creative Commons CC-BY 4.0 License (http://creativecommons.org/licenses/by/4.0/) allowing third parties to copy and redistribute the material in any medium or format, and to remix, transform, and build upon the material, for any purpose, even commercially, under the condition that appropriate credit is given, that a link to the license is provided, and that you indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


Crossref
Scopus
Google Scholar
Europe PMC