Large Language Models (LLMs) are increasingly used in educational settings as interactive tools for collaboration. However, their tendency toward sycophancy, aligning with user beliefs even when incorrect, raises concerns for learning and decision-making, especially for less knowledgeable users. This study investigates how sycophantic alignment emerges in authentic multi-turn human-AI interactions and whether interventions targeting increasing AI literacy and prompting competencies can mitigate its effects. In a controlled mixed-design experiment, 60 participants completed analytical survival ranking tasks by first generating individual rankings and then making final decisions after collaborating with an AI assistant, both before and after receiving either general or sycophancy-focused prompting training. Preliminary results show that LLMs are highly sensitive to user input: lower-quality initial responses lead to poorer AI advice, suggesting that the model mirrors or incorporates user reasoning rather than correcting it or offering better alternatives that are missing or less frequent in the conversation. Critically, the propagation of user errors into AI responses significantly reduced both the quality of AI feedback and final user task performance, revealing a form of contextual sycophantic dependence. While the intervention did not eliminate the propagation of contextual errors, it significantly improved AI advice by reducing the direct mirroring of incorrect user rankings. These findings suggest that prompting and AI literacy alone may be insufficient to ensure epistemically independent AI support, highlighting the need for system-level approaches that better promote critical engagement in human-AI collaboration.

翻译：大语言模型作为交互式协作工具在教育场景中的使用日益广泛。然而，其倾向于谄媚——即使当用户观点错误时也会附和其信念——的特性，对学习和决策过程构成了隐忧，尤其对知识较薄弱的用户更为突出。本研究探讨了在真实的多轮人机交互中这种谄媚式对齐如何显现，以及旨在提升AI素养与提示技能的干预措施能否减轻其影响。在一项受控混合设计中，60名参与者先独立完成分析性生存排序任务，随后在首次生成个人排序后与AI助手协作做出最终决策，之后再接受面向通用或谄媚焦点的提示培训并重复上述流程。初步结果显示，大语言模型对用户输入高度敏感：初始低质量回答会导致AI给出更差的建议，表明模型是镜像或融入了用户推理方式，而非纠正错误或提供对话中缺失/较少出现的最佳替代方案。尤为关键的是，用户错误向AI回答的传播显著降低了AI反馈质量与最终任务表现，揭示了语境层面谄媚依赖性的存在。虽然干预未能消除语境错误的传播，但通过减少对用户错误排序的直接镜像，显著提升了AI建议质量。这些发现表明，单纯依赖提示策略与AI素养可能无法确保AI支持的认知独立性，亟需在系统层面设计促进人机协作中批判性参与的方案。