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Intelligent education relies on the generation of multi-level, comprehensive, and diverse question banks to assess student learning effectiveness and teaching efficacy. However, the development of professional question banks often presents challenges such as reliance on expert knowledge and experience, limited transferability, high workload, and subjective biases. In Geographical Information Systems (GIS), personalized question settings could be impacted by diverse knowledge sources and varying student orientations. To address this issue, we propose a novel large language model (LLM) framework guided by GIS prior knowledge for generating professional GIS question banks. Specifically, we tackle three major challenges in intelligent GIS question bank generation: incomplete knowledge coverage, skewed difficulty distribution, and limited adaptability of question types. This framework is founded upon the autonomous understanding, planning, and reasoning capabilities of LLMs, augmented by an elaborate retrieval strategy. It comprises three key modules: subtask matching and partitioning, subtask importance evaluation and quantity allocation, as well as adaptive scenario question generation. Together, these components enable the generation of personalized GIS question banks for learning and teaching tasks. Extensive experiments demonstrate its effectiveness across various metrics. Furthermore, our method with specialized knowledge organization can serve as a valuable resource for advancing research and applications in GIS education.
Wang et al. (Wed,) studied this question.