The field of AI-supported learning is rapidly evolving, with a growing focus on developing innovative strategies to enhance student engagement, improve learning outcomes, and foster effective human-AI collaboration. Recent studies have explored the potential of large language models (LLMs) to support learning, particularly in areas such as programming education, writing, and self-regulated learning. Notably, research has highlighted the importance of instructional design, pedagogical prompting, and explainable feedback in unlocking the full potential of AI-supported learning tools. Furthermore, the development of theory-driven learning analytics dashboards and metacognitive scaffolding frameworks has shown promise in promoting deeper learning, enhancing conceptual understanding, and developing transferable skills. Overall, the field is moving towards a more nuanced understanding of how AI can be leveraged to support student learning, with a growing emphasis on designing AI-powered tools that complement human instruction and foster more effective learning outcomes. Noteworthy papers include: 'From Generation to Adaptation: Comparing AI-Assisted Strategies in High School Programming Education', which introduces a dual-scaffolding model for effective LCA integration; 'When learning analytics dashboard is explainable: An exploratory study on the effect of GenAI-supported learning analytics dashboard', which demonstrates the significance of explainable feedback in fostering deeper learning; and 'Irec: A Metacognitive Scaffolding for Self-Regulated Learning through Just-in-Time Insight Recall', which proposes a novel paradigm for promoting self-regulated learning through context-triggered insight recall.