Large language models (LLMs) have demonstrated significant potential as educational tutoring agents, capable of tailoring hints, orchestrating lessons, and grading with near-human finesse across various academic domains. However, current LLM-based educational systems exhibit critical limitations in promoting genuine critical thinking, failing on over one-third of multi-hop questions with counterfactual premises, and remaining vulnerable to adversarial prompts that trigger biased or factually incorrect responses. To address these gaps, we propose \textbf{EDU-Prompting}, a novel multi-agent framework that bridges established educational critical thinking theories with LLM agent design to generate critical, bias-aware explanations while fostering diverse perspectives. Our systematic evaluation across theoretical benchmarks and practical college-level critical writing scenarios demonstrates that EDU-Prompting significantly enhances both content truthfulness and logical soundness in AI-generated educational responses. The framework's modular design enables seamless integration into existing prompting frameworks and educational applications, allowing practitioners to directly incorporate critical thinking catalysts that promote analytical reasoning and introduce multiple perspectives without requiring extensive system modifications.

翻译：大语言模型（LLMs）作为教育辅导智能体已展现出巨大潜力，能够在多个学术领域以近乎人类的精细度提供定制化提示、编排课程并进行评分。然而，当前基于LLM的教育系统在促进真正的批判性思维方面存在关键局限：在涉及反事实前提的多跳问题上失败率超过三分之一，并且容易受到对抗性提示的影响，从而产生带有偏见或事实错误的回答。为弥补这些不足，我们提出\textbf{EDU-Prompting}，一种创新的多智能体框架，该框架将成熟的教育批判性思维理论与LLM智能体设计相结合，以生成具有批判性、偏见感知的解释，同时促进多元视角。我们在理论基准和实际大学水平批判性写作场景中的系统评估表明，EDU-Prompting显著提升了AI生成教育响应的内容真实性与逻辑严谨性。该框架的模块化设计使其能够无缝集成到现有提示框架和教育应用中，使实践者能够直接引入促进分析性推理和多元视角的批判性思维催化剂，而无需进行大规模系统修改。