解开预言者之谜：一种“20个问题”游戏以促进人工智能伦理与素养
Demystifying the oracle: A "20 Questions" game to promote AI ethics and literacy
AI总结：
本文通过简化版的“20个问题”游戏，帮助学生理解LLMs的概率本质，促进AI伦理与素养。
摘要：
随着生成式人工智能成为物理教育中的关键组成部分，一个重要的伦理挑战出现了：学生倾向于将大型语言模型（LLMs）拟人化，将其视为权威的“预言者”，认为它们能从内部数据库中检索固定事实。然而，LLMs本质上是概率引擎。本文描述了一种教学活动的设计与实现，即“20个问题”游戏的简化版本，旨在使这种随机性质直接可见。与人类玩家在游戏开始时固定目标物体不同，学生会发现模型根据交互历史的局部一致性生成答案。通过利用重新采样和历史回放等功能，学生充当实验者，观察相同交互历史可以产生分歧的叙述路径。我们讨论如何将这些行为映射到熟悉的物理概念，提供必要的认知支架，以促进有意识的怀疑，将验证AI输出不仅视为合规规则，而是源于系统概率性质的技术必要性。
英文摘要：
As Generative AI becomes a key component in physics education, a significant ethical challenge has emerged: the tendency of students to anthropomorphize Large Language Models (LLMs), treating them as authoritative "oracles" that retrieve fixed facts from an internal database. However, LLMs operate fundamentally as probabilistic engines. This paper describes the design and implementation of a didactic activity, a reduced version of the "20 Questions" game, aimed at making this stochastic nature directly observable. Unlike a human player who fixes a target object at the start of the game, students discover that the model generates answers based solely on local coherence with the interaction history. By utilizing functionalities such as re-sampling and history rewinding, students act as experimenters, observing how identical interaction histories can yield diverging narrative paths. We discuss how mapping these behaviors to familiar physics concepts provides the epistemic scaffolding necessary to promote informed skepticism, framing the verification of AI outputs not merely as a compliance rule, but as a technical necessity derived from the system's probabilistic nature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
