对有学习障碍和没有学习障碍的学生的归因模式:人工智能模型与实习教师相比.
Inbar Levkovich1, Eyal Rabin2, Rania Hussein Farraj3
1Faculty of Education, Tel Hai College, Upper Galilee, Israel.
Research in developmental disabilities
|March 16, 2025
概括
人工智能 (AI) 大型语言模型 (LLM) 比教师对学习障碍 (LD) 的学生表现出更少的丧和更多的同情. 士学位还对所有学生的学术成功表示了更大的乐观.
科学领域:
- 教育心理学教育心理学
- 人工智能伦理学 人工智能伦理学
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 在教育中越来越多地使用,需要了解它们的归因模式.
- 学生的归因 (反,丧,同情,未来的失败期望) 对于学习和包容至关重要.
- 教师和AI对学习障碍 (LD) 的学生和没有学习障碍 (LD) 的学生的反应的差异需要调查.
研究的目的:
- 为了比较四个高级LLM (ChatGPT3.5,ChatGPT4,Claude,Gemini) 的归因模式与澳大利亚和中国的实习教师的归因模式.
- 检查LLM和教师如何对描绘有和没有学习障碍 (LD) 的学生的图片做出反应.
- 评估AI在促进包容性教育前景方面的潜在作用.
主要方法:
- 向LLM和实习教师展示了描述不同能力和努力的学生的八幅小说.
- 在此过程中,LLMs对每个图片进行了十次评估,产生了320个对比分析的评估.
- 实习教师提供了可比的评分,允许直接比较归因模式.
主要成果:
- 与实习教师相比,LLM对LD学生的丧程度较低,对LD学生的同情程度更高.
- 劳工法师表示,他们对LD和非LD学生的未来学术失败的预期低于实习教师.
- 聊天GPT3.5和双子座被评为更多的负面反,而聊天GPT4显示了更多的积极反,与一些教师团体保持一致.
结论:
- 由于较低的判断倾向和更高的乐观情绪,LLM可能会促进LD学生更积极和更具包容性的前景.
- 人工智能工具需要重新校准,以与文化和情感细微差别保持一致,特别是在反交付方面.
- 该研究强调了人工智能在教育中的潜力,同时强调需要仔细实施和持续评估.
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