研究如何根据学生特点设计AI教育反馈，提升修改效果与接受度。
Investigating Learner-Aware Design of LLM-Generated Educational Feedback
- 设计六种不同风格的AI反馈，测试其对答题修订的影响。
- 清晰全面的指导能显著提升修改表现，获多类学生青睐。
- 反馈风格需匹配学生性格，个性化设计更有效。
尽管大语言模型在生成教育反馈方面展现出潜力，但如何设计反馈（如语气和信息覆盖范围）以支持答案修订并被各类学习者接受仍不明确。本文针对选择题生物题目设计了六种反馈方案，包括一种基线设计及引入额外元素的变体，并通过高中生实验进行实证研究。采用即时修订表现和六项主观评估标准进行评价，分析基于人格特质的学习者群体对反馈偏好的差异。结果表明，清晰且全面的指导能提升修订表现，并获得跨群体的正面评价；而信息新颖性和情感基调则在不同学习者中表现出差异。研究提示，在设计由大语言模型生成的反馈时，应考虑学习者画像。
原文摘要 · Abstract (English)
Although large language models (LLMs) show promise for generating educational feedback, it remains unclear how feedback should be designed (e.g., tone and information coverage) to support answer revision and learner acceptance across diverse learner profiles. We define six feedback designs for multiple-choice biology questions, including a baseline design and variants with additional feedback elements, and conduct an empirical study with high school students. We evaluate feedback using immediate revision performance and six subjective evaluation criteria, and analyze how feedback preferences vary across learner profiles based on personality traits. Our results show that feedback with clear and comprehensive guidance improves revision performance and receives favorable evaluations across learner profiles, whereas informational novelty and affective framing vary across profiles. These findings suggest that learner profiles should be considered when designing LLM-generated feedback.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。