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This paper presents INSIDE, a framework that fine-tunes LLMs to generate internal dialogue grounded in Bloom's Taxonomy, enabling student simulators to model both latent reasoning and observable actions. Evaluations show improved action fidelity and reasoning alignment compared to prompting baselines.
本文通过布鲁姆分类学评估了六个LLM，以判断其生成能激发高阶思维的教育问题的能力，并引入了一种提示策略，该策略使重复率降低了24.45%，高阶输出增加了11.53%。