A Scoping Review of Functional Near-Infrared Spectroscopy (fNIRS) Applications in Game-integrated Learning Systems

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a valuable tool to investigate cognitive and emotional processes during learning. We focus specifically on game-integrated learning systems as the context for fNIRS-based brain data analysis. We selected game-integrated learning systems because such systems make learning more engaging, interactive, and immersive, all of which are critical features for adaptive learning design. The goal of this scoping review is to help researchers understand how fNIRS has been used so far to study brain activity in game-integrated learning systems. We also aim to show how brain data captured through fNIRS can support the development of adaptive learning systems by monitoring learners' cognitive states. Using the PRISMA-ScR framework, 1300 papers were screened, and 21 empirical studies were selected for in-depth analysis. Studies were categorized as affective/cognitive response studies or comparative studies, and further analyzed by learning platform, game device, fNIRS configuration, outcome measures, and study design. The findings reveal that game-integrated learning systems can be as effective as traditional methods in improving engagement and involvement. The findings also show that fNIRS offers valuable insights into cognitive states, but it has not yet been widely implemented in real-time adaptive systems. We identify key challenges in standardization and data interpretation and highlight the potential of fNIRS for developing brain-aware, interactive learning environments. This review offers insights to guide future research on using brain data to support adaptive learning and intelligent system design.