AI-Enabled Serious Games: Integrating Intelligence and Adaptivity in Training Systems
Priyamvada Tripathi, Bill Kapralos

TL;DR
This paper explores how AI techniques like LLMs and reinforcement learning can enhance real-time adaptation and intelligence in serious games, addressing current limitations and challenges.
Contribution
It provides a historical overview of adaptive learning systems and discusses how modern AI approaches can improve serious game adaptivity and intelligence.
Findings
AI enables dynamic scenario variation and contextual feedback.
Integration of AI raises issues of validity, transparency, and trust.
Limited empirical evidence on long-term learning outcomes.
Abstract
Serious games are widely used for learning and training across domains such as healthcare, defense, and education. Persistent challenges remain, however, including static scenario design, authoring bottlenecks, limited learner modeling, and difficulty implementing meaningful real-time instructional adaptation. Recent advances in artificial intelligence (AI) introduce novel capabilities such as dynamic scenario variation, contextual feedback, adaptive pacing, and learner-state modeling that may help address some of these limitations. At the same time, integrating AI into serious games raises important questions related to validity, transparency, system control, and learner trust. This chapter examines how contemporary AI approaches may support real-time instructional adaptation in serious games. It distinguishes between instructional intelligence, defined as a system's capacity to infer…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
