An RL‐Driven Adaptive Game Approach to Support Cultural Heritage Learning.
Background Study: Serious games for cultural heritage offer opportunities for enhancement, particularly in user experience and educational impact. This paper presents a Reinforcement Learning (RL)‐driven adaptive approach to support CH learning through a graphic adventure game. Objectives: The propo...
| Publicado en: | Journal of Computer Assisted Learning Vol. 42; no. 1; pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191181609&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191181609 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2026 vid: 42 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191181609 191181609 191181609 10.1111/jcal.70156 191181609 ppf: 1 ppct: 15 formats: tig: atl: An RL‐Driven Adaptive Game Approach to Support Cultural Heritage Learning. aug: au: Tsita, Christina Gogos, Christos Dimitriou, Nikolaos Tzovaras, Dimitrios Satratzemi, Maya affil: Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece sug: subj: Learning Methods Gamification Machine Learning Algorithms Educational Technology Computer-Assisted Instruction Support, Psychosocial Culture Human Funding Source Learning Environment Conceptual Framework Video Games Experiential Learning ab: Background Study: Serious games for cultural heritage offer opportunities for enhancement, particularly in user experience and educational impact. This paper presents a Reinforcement Learning (RL)‐driven adaptive approach to support CH learning through a graphic adventure game. Objectives: The proposed solution integrates an artificial intelligence service based on the Expected State‐Action‐Reward‐State‐Action (SARSA) algorithm to provide dynamic, timely, and personalised in‐game support. This service adjusts the Level of Detail (LoD) in the assistance offered to players, promoting an engaging learning experience tailored to individual needs and pacing. Methods: The educational game, ForumSG, reconstructs the Roman Forum of Thessaloniki, offering exploratory learning within a 3D virtual environment. An in‐game activity—the lost‐wax method of coin production—was used to evaluate the adaptive service with both simulated and real users. The game collects user behavior data and uses the RL service to determine when and how to provide support, for an optimal balance between challenge and skill. Results: Experimental testing with synthetic users showed that the Expected SARSA agent successfully learned behaviorally consistent action‐selection policies, favouring lower LoD in uniform‐probability settings and higher LoD in more complex states, driven by reward function and transition efficiency. A preliminary pilot assessment with real users validated the functionality of the RL system. The game was well received, with participants appreciating the timely and supportive guidance, exploratory nature of the experience and its cognitive engagement. Conclusion: This work contributes to the field by combining pedagogical, museological, and technological design principles in Cultural Heritage Serious Games. The developed RL architecture is lightweight compared to deep learning methods, allowing for easy integration into other educational games, and providing a foundation for scalable, personalised CH learning systems. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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