This position paper explores the integration of generative AI (GenAI) with adaptive learning (AL) in education. GenAI, characterized by its ability to generate new content based on learned patterns, has seen significant advancements in areas like natural language processing (NLP) and computer vision (CV). Adaptive learning, on the other hand, enhances student learning efficiency by tailoring educational experiences to individual needs. The paper argues that combining GenAI with AL can significantly advance educational formats by leveraging GenAI's dynamic and diverse output capabilities. The paper discusses the benefits, challenges, and potential of this integration, emphasizing the following key points: 1. **Empowerment of Existing Algorithms**: GenAI can enhance existing AL algorithms by improving profile building and material recommendation. For instance, GenAI can generate more accurate and personalized learning profiles and provide more diverse and engaging learning materials. 2. **Establishing Novel Directions**: GenAI's generative capabilities open new avenues for content creation, intelligent agent development, and learning simulation. For example, GenAI can create high-fidelity content, serve as intelligent agents for personalized support, and generate synthetic data for training. 3. **Impacts on AL**: The integration of GenAI offers benefits such as diversity and dynamics in learning experiences, multi-modality convenience, and powerful prior knowledge. However, it also presents challenges like hallucination, capability decay, fairness issues, and the need for coevolution with human education. The paper concludes by highlighting the potential of GenAI in advancing AL, while also acknowledging the importance of addressing ethical and practical challenges. It calls for further research and development to ensure that GenAI enhances educational outcomes without compromising fairness and human-centric goals.This position paper explores the integration of generative AI (GenAI) with adaptive learning (AL) in education. GenAI, characterized by its ability to generate new content based on learned patterns, has seen significant advancements in areas like natural language processing (NLP) and computer vision (CV). Adaptive learning, on the other hand, enhances student learning efficiency by tailoring educational experiences to individual needs. The paper argues that combining GenAI with AL can significantly advance educational formats by leveraging GenAI's dynamic and diverse output capabilities. The paper discusses the benefits, challenges, and potential of this integration, emphasizing the following key points: 1. **Empowerment of Existing Algorithms**: GenAI can enhance existing AL algorithms by improving profile building and material recommendation. For instance, GenAI can generate more accurate and personalized learning profiles and provide more diverse and engaging learning materials. 2. **Establishing Novel Directions**: GenAI's generative capabilities open new avenues for content creation, intelligent agent development, and learning simulation. For example, GenAI can create high-fidelity content, serve as intelligent agents for personalized support, and generate synthetic data for training. 3. **Impacts on AL**: The integration of GenAI offers benefits such as diversity and dynamics in learning experiences, multi-modality convenience, and powerful prior knowledge. However, it also presents challenges like hallucination, capability decay, fairness issues, and the need for coevolution with human education. The paper concludes by highlighting the potential of GenAI in advancing AL, while also acknowledging the importance of addressing ethical and practical challenges. It calls for further research and development to ensure that GenAI enhances educational outcomes without compromising fairness and human-centric goals.