Computational Advances in Artificial Intelligence and Game Science for Education

Dear Colleagues,

Artificial Intelligence (AI) is no longer an abstract promise for the future of education; rather, it is actively reshaping how knowledge is created, mediated, and experienced. Advances in generative models, reinforcement learning, and multimodal analytics are opening new frontiers for intelligent tutoring, adaptive feedback, and learning environments that respond dynamically to learners’ actions and contexts. Serious games and immersive simulations are at the forefront of this transformation, serving as powerful testbeds for AI innovation where algorithms, data, and design converge to create adaptive, engaging, and personalised educational experiences. These developments signal a shift towards a novel computational paradigm in education, where systems are not only designed by educators but also co-constructed through algorithms, models, and AI-driven architectures. To fully realise this potential, it is essential to investigate the technical foundations, such as algorithms, system architectures, and computational methodologies, that enable these innovations, and to envision how they can be harnessed for scalable, ethical, and transformative educational futures. 

Focus Areas

1. Algorithms and Models for AI in Education and Games

• Machine learning and deep learning models for adaptive learning.
• Reinforcement learning for personalised feedback and scaffolding.
• Generative AI models (text, image, video, and simulation) for learning content and game-based experiences.
• Hybrid modelling approaches (e.g., combining rule-based, statistical, and neural methods).
• Player and learner modelling: affect detection, engagement prediction, and knowledge tracing.

2. Systems, Architectures, and Computational Infrastructures

• Intelligent tutoring systems: architectures, pipelines, and orchestration engines.
• Technical challenges in large-scale AIED deployment (scalability, computational efficiency, and interoperability).
• System robustness, reliability, and security in AI-enhanced learning platforms.
• Data-driven infrastructures: multimodal analytics, sensor integration, and real-time data pipelines.
• Cloud-based, distributed, and edge-computing solutions for AIED.

3. Applications in Serious Games and Game Science

• Computational design of AI-driven serious games and game-based learning environments.
• Procedural content generation, adaptive difficulty balancing, and co-creative GenAI in games.
• Simulation-based learning with VR/AR/XR, AI-based storytelling, and immersive analytics.
• Autonomous assessment and feedback systems embedded in game environments.
• Cross-disciplinary applications of AI and games (STEM, humanities, and arts) emphasising technical implementations.

Dr. Petros Lameras
Dr. Panagiotis Petridis
Prof. Dr. Sylvester Arnab
Guest Editors

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• artificial intelligence
• education
• serious games
• immersive simulations
• STEM