This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature extraction, hearing loss modeling, and the use of emerging architectures for long-sequence processing. Listener-specific adaptation strategies and domain generalization approaches that aim to improve robustness in unseen acoustic environments are also discussed. Remaining challenges, such as the need for large-scale, diverse datasets and reliable cross-profile generalization, are acknowledged. Our goal is to offer a perspective on current trends, ongoing challenges, and possible future directions toward practical and reliable HA-oriented intelligibility prediction systems.