生成式人工智能与批判性思维：思维伙伴干预的约束优先设计试点
Critical Thinking with Generative AI: A Constraint-First Design Pilot of a Thinking-Partner Intervention
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中文总结 AI 辅助
本研究通过设计试点，在本科统计课程中利用ChatGPT作为思维伙伴，发现GenAI辅助教学能提升领域学习和AI素养，但未改善标准化批判性思维，学生多将模型用于验证而非对话。
中文摘要 AI 辅助
生成式人工智能（GenAI）工具进入高等教育课堂的速度超过了该领域研究其对学习影响的能力。一个担忧是，GenAI 可能会取代学生毕业后所需的批判性思维和人工智能素养。本文报告了一项基于设计的研究试点，该试点采用 GenAI 辅助的批判性思维框架，在 2025 年春季学期的本科生研究方法和统计课程中，将 ChatGPT 用作思维伙伴（N = 14）。混合方法设计结合了干预前后对统计学习（AASCDM）、人工智能素养（MAILS）和批判性思维（WGCTA）的测量，以及教师现场笔记、学生作品和学生与人工智能交互日志。前后测结果显示，AASCDM 的每个维度均有提升，MAILS 的九个维度中有八个有所提升，而 WGCTA 的百分位数没有变化。定性分析确定了四个主题：学生定位大语言模型的方式（作为答案生成器、验证者或共同思考者）；学生参与深度（程序性 vs. 概念性）；偶尔将工具拟人化；以及课程设计在塑造上述三个方面中的作用。综合来看，研究结果表明，一个学期的 GenAI 辅助教学可以促进领域学习和自我报告的人工智能素养，但不会改变标准化的批判性思维，并且学生与大语言模型的主要关系是验证而非对话。最后，我们提出了下一轮框架的设计原则，以及对自适应和个性化学习研究的意义。
英文摘要
Generative AI (GenAI) tools entered higher education classrooms faster than the field was able to study their effects on learning. One concern is that GenAI may displace the critical thinking and AI literacy that students will need after graduation. This paper reports a Design-Based Research pilot of a GenAI-assisted critical thinking framework, in which ChatGPT was used as a thinking partner in an undergraduate research methods and statistics course during Spring 2025 (N = 14). The mixed-methods design combined pre- and post-intervention measures of statistical learning (AASCDM), AI literacy (MAILS), and critical thinking (WGCTA) with instructor field notes, student artifacts, and student-AI interaction logs. Pre-post tests showed gains on every AASCDM dimension and on eight of nine MAILS dimensions, while WGCTA percentiles did not change. Qualitative analysis identified four themes: the ways students positioned the LLM (as answer generator, validator, or co-thinker); the depth of student engagement (procedural vs. conceptual); occasional humanizing of the tool; and the role of curriculum design in shaping each of the prior three. Read together, the findings indicate that one semester of GenAI-assisted instruction can move domain learning and self-reported AI literacy but does not move standardized critical thinking, and that the modal student-LLM relationship is one of validation instead of dialogue. We end with design principles for the next iteration of the framework and implications for research on adaptive and personalized learning.
发表机构
- University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
- The Ohio State University（俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。
