Priyamvada Tripathi, Bill Kapralos (2026) — Ontario Tech University. Springer book chapter, Advances in Global Applied Artificial Intelligence.
Summary
Serious games are widely used for learning and training across domains such as healthcare, defense, and education. This chapter examines how contemporary AI approaches may support real-time instructional adaptation in serious games.
Key Findings
This book chapter provides a comprehensive survey of AI integration in serious games for learning and training, distinguishing between instructional intelligence (inferring learner knowledge and reasoning about pedagogically appropriate responses) and adaptivity (modifying instructional actions during interaction). The authors trace the historical evolution from early computer-assisted instruction through Intelligent Tutoring Systems, dynamic difficulty adjustment, authoring platforms, and learning analytics to contemporary AI-enabled architectures. Three AI technologies are identified as having high potential: large language models (LLMs), reinforcement learning (RL), and agent-based architectures. The chapter also highlights critical challenges including explainability, validation, computational cost, and the limited empirical evidence regarding long-term learning outcomes in AI-enabled serious games. This survey connects to intelligent-tutoring research by framing serious games as an application domain for ITS principles, complementing the multimodal feedback architectures demonstrated in multimodal-affective-its-presentation. The discussion of LLM and agent-based integration aligns with agentic-ai-ecosystems-higher-education perspectives on multi-agent AI frameworks, while the emphasis on empirical validation gaps resonates with calls for rigorous efficacy-study research across AIED. The focus on training transfer extends adaptive-learning-systems into professional and defense applications beyond K-12 and higher education.
Related Pages
- programming-its — Frames serious games as an ITS application domain with distinct adaptivity requirements
- multimodal-affective-its-presentation — Complements multimodal ITS architectures with game-based training contexts
- agentic-ai-ecosystems-higher-education — LLM and agent-based integration in serious games mirrors multi-agent AI frameworks
- adaptive-learning-systems — Extends adaptive learning principles to game-based training across professional domains
- ai-k12-evidence-base — Highlights critical gap in empirical evidence for long-term learning outcomes in AI-enabled games
Citation
APA: Priyamvada Tripathi, Bill Kapralos (2026). AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems. arXiv:2605.21962. Springer book chapter, Advances in Global Applied Artificial Intelligence.