## 5 Limitations of and Optimization Strategies for AI-Driven High School Geography Research Design
### Limitations of AI in High School Geography Research  
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2. Insufficient technical adaptability  
AI technology can achieve data analysis, dynamic feedback, and personalized support in geographical research and study, but its effectiveness is highly dependent on high-quality data input and the support of technical platforms. In ecological monitoring and geographic information system (GIS) applications, real-time updated data are particularly needed. However, in some remote areas, environmental data may be missing or inaccurate [46]. This problem is particularly prominent in the context of rural revitalization. Rural primary schools in some areas may face the problem of incomplete data collection, which affects the application effect of AI systems. For example, in the study activities of migratory bird habitats in the Yellow (Bohai) Sea, wetland ecological monitoring data may not cover all key nodes, which directly leads to certain deviations in the ecological model predictions generated by AI [47]. In this way, although AI can process and analyze large amounts of data, if there are loopholes or errors in the basic data, the final analysis results may be inaccurate, thus affecting students' understanding of and practice in the ecosystem. In addition, the use of AI technology is highly dependent on the internet and high-performance devices [35]. In research and study activities, students need to access and operate AI through internet connections and advanced devices, but in some cases where the network is unstable or the equipment is in poor condition, all functions in the research and study design may not be realized. This technical dependence limits the widespread application of AI in different regions and schools, especially in some resource-scarce regions, where the advantages of AI technology in education may not be fully utilized. 2. Differences in students' abilities and learning habits  
Although AI technology can support personalized learning, its application effect is still affected by students' basic abilities and learning habits [48]. Some students may lack the necessary data analysis skills or have insufficient understanding of AI, which may limit their participation and learning effects in research and study activities [3]. In particular, when operating AI, students of different abilities may face different difficulties [49]. For example, in the process of ecological model design and data visualization, some students may not fully understand the generated results, which affects their in-depth understanding and practical application of the learning content [50]. In addition, some students are overly dependent on AI-generated feedback, which may weaken their ability to think independently and practice [51]. Although AI can provide rapid analysis and feedback, over-reliance on AI may lead to students' lack of independent problem-solving ability, which in turn affects their critical thinking and innovation ability [52]. 3. Transformation and adaptation of teachers' roles  
The introduction of AI technology has placed higher demands on the role of teachers. Teachers not only need to master traditional teaching skills, but also need to be familiar with the operation and application of AI technology and be able to effectively guide students to use AI to complete tasks [53]. However, some teachers may face challenges in adapting and applying technology, especially in highly technical aspects such as data analysis and model design [54]. The existence of technical barriers means that the depth of teachers' understanding of AI directly affects the effectiveness of teaching design [50]. If technical training is insufficient, teachers may find it difficult to fully utilize the functions of AI, which will affect students' learning experience and the overall effect of research and study activities. At the same time, the deep involvement of AI has also posed new challenges to the role of teachers.