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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.
This paper evaluates six LLMs through Bloom's Taxonomy to assess their ability to generate educational questions that stimulate higher-order thinking, introducing a prompting strategy that reduces repetitiveness by 24.45% and increases higher-order outputs by 11.53%.