Yujie Tang

This study investigates the potential and limitations of applying generative artificial intelligence (AIGC) to the development of contextualized science test questions for junior high schools based on the SOLO taxonomy. By comparing one-step and step-by-step prompting strategies, SOLO-based test questions on the topic of "properties of carbon dioxide" were generated. The analysis focuses on AIGC's performance in context design, question chain construction, and progression of thinking levels. The findings indicate that step-by-step instructions more effectively guide AIGC to generate novel, structured, and logically coherent contexts and question chains. However, issues such as lack of scientific accuracy, overly explicit prompts, and weak logical connections between sub-questions remain. Therefore, AIGC can serve as an efficient auxiliary tool for test question development, providing creative ideas and initial drafts, but teachers are still required to conduct scientific validation, logical restructuring, and language optimization to achieve human-AIGC collaboration. Future efforts to further improve AIGC-generated question quality may include providing positive and negative examples, using step-by-step iterative instructions, and embedding domain-specific knowledge constraints.

generative artificial intelligence, SOLO taxonomy, contextualized test questions

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