While Large Language Models (LLMs) are increasingly envisioned as intelligent assistants for personalized learning, systematic head-to-head evaluations in authentic learning scenarios remain scarce. This study presents an empirical comparison of three state-of-the-art LLMs on a tutoring task simulating a realistic learning setting. Using a dataset containing a student's responses to ten mixed-format questions with correctness labels, each model was asked to (i) analyze the quiz to identify underlying knowledge components, (ii) infer the student's mastery profile, and (iii) generate targeted guidance for improvement. To mitigate subjectivity and evaluator bias, Gemini was employed as a virtual judge to perform pairwise comparisons across multiple dimensions: accuracy, clarity, actionability, and appropriateness. Results analyzed via the Bradley-Terry model reveal that GPT-4o is generally preferred, producing feedback that is more informative and better structured than its counterparts, whereas DeepSeek-V3 and GLM-4.5 demonstrate intermittent strengths but lower consistency. These findings highlight the feasibility of deploying LLMs as advanced teaching assistants for individualized support and provide methodological insights for subsequent empirical research on LLM-driven personalized learning.

翻译：尽管大语言模型（LLMs）日益被视为个性化学习的智能助手，但在真实学习场景中进行的系统性直接对比评估仍然匮乏。本研究在模拟真实学习环境的辅导任务上，对三种前沿大语言模型进行了实证比较。利用一个包含学生对十道混合题型作答及其正确性标签的数据集，我们要求每个模型执行以下任务：（i）分析测验以识别潜在知识构成，（ii）推断学生的掌握情况画像，以及（iii）生成针对性的改进指导。为减少主观性和评估者偏差，本研究采用Gemini作为虚拟裁判，在准确性、清晰度、可操作性及适切性等多个维度上进行两两比较。通过Bradley-Terry模型分析的结果表明，GPT-4o总体上更受青睐，其生成的反馈比同类模型更具信息量且结构更优；而DeepSeek-V3与GLM-4.5虽展现出间歇性优势，但一致性较低。这些发现凸显了部署大语言模型作为高级教学助手以提供个性化支持的可行性，并为后续基于大语言模型的个性化学习实证研究提供了方法论启示。