Large language models (LLMs) show promise for automatically generating feedback in education settings. However, it remains unclear how specific feedback elements, such as tone and information coverage, contribute to learning outcomes and learner acceptance, particularly across learners with different personality traits. In this study, we define six feedback elements and generate feedback for multiple-choice biology questions using GPT-5. We conduct a learning experiment with 321 first-year high school students and evaluate feedback effectiveness using two learning outcomes measures and subjective evaluations across six criteria. We further analyze differences in how feedback acceptance varies across learners based on Big Five personality traits. Our results show that effective feedback elements share common patterns supporting learning outcomes, while learners' subjective preferences differ across personality-based clusters. These findings highlight the importance of selecting and adapting feedback elements according to learners' personality traits when we design LLM-generated feedback, and provide practical implications for personalized feedback design in education.

翻译：大型语言模型（LLM）在教育场景中自动生成反馈展现出巨大潜力。然而，特定反馈要素（如语气和信息覆盖度）如何影响学习成效与学习者接受度，特别是对具有不同人格特质的学习者而言，目前尚不明确。本研究定义了六种反馈要素，并利用GPT-5为多项选择题生物学问题生成反馈。我们通过对321名高一学生开展学习实验，采用两项学习成效指标和六项主观评价标准评估反馈效果。进一步基于大五人格特质分析了学习者对反馈接受度的差异。结果表明，有效的反馈要素在促进学习成效方面存在共性模式，而学习者的主观偏好则因人格聚类而异。这些发现强调，在设计LLM生成反馈时，根据学习者人格特质筛选和调整反馈要素至关重要，并为教育领域的个性化反馈设计提供了实践启示。