发表机构
ETH Zürich; MIT Media Lab（苏黎世联邦理工学院; 麻省理工学院媒体实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比学习情境中三种AI交互策略，发现不受限制的ChatGPT式机器人学习增益更高，自适应辅导系统脑电参与度显著更高，且前者的成功源于即时后测而非深层学习
AI 中文摘要
不受限制的AI访问是会绕过学习所需的认知努力，还是会简化知识获取？本文报告了一项研究，在学习情境中比较了三种用户-AI交互设计：（1）类似ChatGPT的不受限制的对话机器人；（2）受教学约束的机器人，通过提示引导而非给出最终答案，我们称之为苏格拉底模式；（3）非对话式自适应辅导系统，基于脑信号推导的用户认知参与度实时调整难度。五十名研究参与者被要求学习核安全协议，该领域被选为零先验知识基线。参与者依次完成教学视频、预测试、AI驱动的评估阶段（三种条件下有所不同）和即时后测试。问题性质主要围绕事实知识获取，但仍要求参与者具备对概念的整体理解才能正确作答。Muse头带被用于推导所有条件下所有用户的认知参与度。不受限制的聊天机器人产生的学习增益（delta）高于两种受约束模式（p <.03，d > 0.80），而自适应条件产生的EEG参与度显著更高（p =.018）。对用户聊天机器人使用和讨论模式的聚类分析显示，多数不受限制模式下的参与者采用直接答案检索策略，而苏格拉底模式下的参与者最初尝试通过提示推理，随后逐渐弃权（不执行）。因此，这也表明不受限制AI的成功并非更深层次学习的证据，而是训练阶段后即时后测试的结果。
英文摘要
Does unrestricted AI access bypass the cognitive effort required for learning, or does it streamline knowledge acquisition? This paper reports on a study where we compare three designs for user-AI interaction in a learning context: (1) an unrestricted conversational bot like ChatGPT, (2) a pedagogically constrained bot that guides through hints without giving final answers, which we refer to as the Socratic mode; and (3) a non-conversational adaptive tutoring system that adjusts difficulty in real-time based on the user's cognitive engagement derived from the brain signals. Fifty study participants were tasked with learning about nuclear safety protocols, a domain chosen for its zero-prior knowledge baseline. The participants progressed through an instructional video, a pre-test, an AI-driven assessment phase, which varied in the three conditions, and an immediate post-test. The nature of the questions centered primarily on factual knowledge acquisition, but it still required participants to have a global understanding of the concepts in order to answer the questions correctly. A Muse headband was used to derive the cognitive engagement of all users in all conditions. The unrestricted chatbot produced higher learning gains (delta) than both constrained modes (p < .03, d > 0.80), while the adaptive condition generated significantly higher EEG engagement (p = .018). The cluster analysis of chatbot usage and discussion patterns by users showed that most participants in the unrestricted-mode adopted a direct answer-retrieval strategy, while participants in the Socratic-mode initially attempted to reason through the hints before progressively disengaging. Consequently, this also suggests that the success of the unrestricted AI is not an evidence of deeper learning, but rather a result of the immediate post-test evaluation after the training phase.
Comments11 pages, 2 figures, 3 tables, to appear at The 14th International Conference on Human-Agent Interaction (HAI'26)
