AI 中文总结
研究探讨在STEM教育中提示不仅是技术能力，更是认知实践。基于认识论框架等提出认知提示新框架及多轮循环，形成提示框架轨迹，阐述其构建过程，还讨论了对人工智能介导的STEM教学及学习者与大语言模型交互的影响。
AI 中文摘要
提示工程通常被视为一种技术能力，用于从大语言模型（LLMs）获得更准确、相关或格式良好的输出。然而，在STEM教育中，提示也应被理解为一种持续的认知实践。学生解读情境和学科线索，并对何种知识、表示和行动合适形成期望。本文借鉴认识论框架和人工智能介导的概念到决策推理，提出了一种名为认知提示的新框架，并提出了一个多轮的框架-提示循环。教育相关成果是一个提示框架轨迹：知识任务发展过程中提示、模型响应、学习者吸收、学科检查和重新框架移动的序列。在此框架中，初始提示通过选择问题、表示、假设、标准以及学习者和模型之间的工作分配来建立一个临时宏观框架。随后每个学习者轮次可以维持、指定、挑战、修复或转换该组织。还讨论了对人工智能介导的STEM教学，特别是对学习者与大语言模型交互的影响。
英文摘要
Prompt engineering is commonly presented as a technical competence for obtaining more accurate, relevant, or well-formatted outputs from large language models (LLMs). However, in STEM education, prompting should also be understood as a continuous epistemic practice. Students interpret contextual and disciplinary cues and adopt expectations about what kind of knowledge, representation, and action are appropriate. Drawing on epistemological framing, and AI-mediated concept-to-decision reasoning, the paper presents a new framework called epistemic prompting and proposes a multi-turn Framing-Prompting Loop. The educationally relevant outcome is a prompt framing trajectory: the sequence of prompts, model responses, learner uptake, disciplinary checks, and reframing moves through which a knowledge task develops. In this framework, an initial prompt establishes a provisional macro-frame by selecting the problem, representations, assumptions, criteria, and distribution of work between learner and model. Each subsequent learner turn can then maintain, specify, challenge, repair, or transform that organization. The implications for AI-mediated STEM instruction, and, specifically, on learner-LLM interaction are also discussed.
Comments13 pages, 1 figure, 2 tables
