Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment. While recent studies show the promise of using large language models (LLMs) for simulating human behavior, such approaches have not gone beyond rudimentary proof-of-concept stages due to key limitations. First, LLMs are highly sensitive to minor prompt variations, raising doubts about their ability to generalize to new scenarios without extensive prompt engineering. Moreover, apparently successful outcomes can often be unreliable, either because domain experts unintentionally guide LLMs to produce expected results, leading to self-fulfilling prophecies; or because the LLM has encountered highly similar scenarios in its training data, meaning that models may not be simulating behavior so much as regurgitating memorized content. To address these challenges, we propose Hyp-Mix, a simulation authoring framework that allows experts to develop and evaluate simulations by combining testable hypotheses about learner behavior. Testing this framework in a physics learning environment, we found that GPT-4 Turbo maintains calibrated behavior even as the underlying learner model changes, providing the first evidence that LLMs can be used to simulate realistic behaviors in open-ended interactive learning environments, a necessary prerequisite for useful LLM behavioral simulation.

本研究针对大型语言模型（LLMs）在开放式互动学习环境中的应用局限性进行了探讨，提出了Hyp-Mix框架，以解决提示微小变化导致的反馈不一致问题。通过在物理学习环境中的测试，我们发现GPT-4 Turbo能够保持适当的行为表现，为LLMs在开放式学习环境中模拟真实行为的有效性提供了初步证据。此项研究为LLMs的教育应用打开了新的可能性。 

大型语言模型能否可靠地模拟人类学习者行为？开放式学习环境的模拟创作框架