Longitudinal analysis compares large language models' efficacy in mathematics exam creation, suggesting improvements over time.
The study presents a longitudinal and comparative analysis of Large Language Model (LLM) in mathematics exam creation processes based on the Revised Bloom’s Taxonomy (RBT). The study focuses on three widely used free applications: ChatGPT, Gemini, and Claude. In this study, conducted at the secondary school level, the features expected in mathematics written exam questions (scientific relevance, target relevance, and understandability) were examined in terms of their conformity to the RBT steps. The study was conducted with data obtained from LLMs in 2024 and 2026. The results revealed the changes in LLMs over the 2-year period and the differences in the taxonomy levels and question-writing skills. Within the scope of the questions analysed, it was determined that the advantages and limitations of the LLMs changed over time, and that questions targeting especially the upper levels of the taxonomy showed improvement after the second prompt in both years. The results highlight the potential of LLM as instructional tools, which still require human control, and underscore the need for continuous evaluation and validation as these technologies evolve.
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Şahin et al. (2026) studied this question.
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