Lightweight CNN-RL Adaptive Feedback for Gamified Learning Platforms: A Review

Abstract

The rapid increase in online and blended learning has added pressure on the necessity to have adaptive, engaging,
and efficient educational technologies. This paper provides a review and syntheses of the current developments
in lightweight convolutional neural networks (CNNs) and reinforcement learning (RL) to support the
establishment of real-time, personalised feedback in a gamified learning setting. The paper presents the theoretical
premises, system designs, and experimental findings of the recent designs, and emphasises the potential and
difficulties of implementing these systems to resource-constrained platforms. As we have shown in the analysis,
the combination of lightweight CNNs with RL agents can allow adaptive learning to become scaled, responsive,
and efficient, and become the foundation of the intelligent education platform of the next generation.