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arXiv 2507.00007cs.CYcs.AIcs.LG
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将通用生成式人工智能平台整合到教育实验室中以培养批判性思维与数字素养

Integrating Universal Generative AI Platforms in Educational Labs to Foster Critical Thinking and Digital Literacy

Borough of Manhattan Community College(曼哈顿社区学院);The City University of New York(纽约市立大学)

Vasiliy Znamenskiy, Rafael Niyazov, Joel Hernandez

AI总结:

本文提出将 ChatGPT 等 GenAI 平台整合进教育实验室,作为研究对象与认知工具以培养本科生批判性思维与数字素养,试点表明结合反思性评估的结构化 AI 交互能改善学习成果,并提供了可跨学科复制的模型。

摘要:

本文提出了一个新的教育框架,旨在将生成式人工智能(GenAI)平台(如 ChatGPT、Claude 和 Gemini)整合到实验室活动中,以培养本科生的批判性思维与数字素养。认识到不加批判地依赖大型语言模型(LLMs)的局限性与风险,本文提出的教学模型将 GenAI 重新定位为研究对象与认知工具。学生制定特定学科的提示词,并评估 GenAI 在文本、图像和视频模态下生成的回复。在面向非理科专业学生的普通天文学课程中进行的试点实施表明,学生表现出了高度的参与度与批判性反思,许多学生在课后继续开展该活动,并在研究研讨会上展示了成果。结果突显了结构化人工智能交互在教育中的重要性,并表明 GenAI 与反思性评估方法相结合时能够改善学习成果。该研究提出了一个适用于跨学科人工智能整合实验室工作的可复制模型,可适应不同的科学学科。参见基于生成式人工智能平台的学习活动指南:https://doi.org/10.5281/zenodo.15555802

英文摘要:

This paper presents a new educational framework for integrating generative artificial intelligence (GenAI) platforms such as ChatGPT, Claude, and Gemini into laboratory activities aimed at developing critical thinking and digital literacy among undergraduate students. Recognizing the limitations and risks of uncritical reliance on large language models (LLMs), the proposed pedagogical model reframes GenAI as a research subject and cognitive tool. Students formulate discipline-specific prompts and evaluate GenAI-generated responses in text, image, and video modalities. A pilot implementation in a general astronomy course for non-science majors demonstrated high levels of engagement and critical reflection, with many students continuing the activity after class and presenting results at a research symposium. The results highlight the importance of structured AI interactions in education and suggest that GenAI can improve learning outcomes when combined with reflective assessment methods. The study proposes a replicable model for interdisciplinary AI-integrated lab work, adaptable to scientific disciplines. See the guide to learning activities based on Generative-Ai platforms: https://doi.org/10.5281/zenodo.15555802

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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