This paper introduces Prompt-to-Primal (P2P) Teaching, an AI-integrated instructional approach that links prompt-driven exploration with first-principles reasoning, guided and moderated by the instructor within the classroom setting. In P2P teaching, student-generated AI prompts serve as entry points for inquiry and initial discussions in class, while the instructor guides learners to validate, challenge, and reconstruct AI responses through fundamental physical and mathematical laws. The approach encourages self-reflective development, critical evaluation of AI outputs, and conceptual foundational knowledge of the core engineering principles. A large language model (LLM) can be a highly effective tool for those who already possess foundational knowledge of a subject; however, it may also mislead students who lack sufficient background in the subject matter. Results from two student cohorts across different semesters suggest the pedagogical effectiveness of the P2P teaching framework in enhancing both AI literacy and engineering reasoning.

翻译：本文介绍了一种人工智能融合的教学方法——提示到原理（P2P）教学法，该方法在课堂环境中，由教师引导和调节，将提示驱动的探索与第一性原理推理联系起来。在P2P教学中，学生生成的人工智能提示作为课堂探究和初步讨论的切入点，同时教师引导学习者通过基本的物理和数学定律来验证、质疑和重构人工智能的回应。该方法鼓励自我反思式发展、对人工智能输出的批判性评估，以及对核心工程原理的概念性基础知识的掌握。对于已经掌握学科基础知识的学习者而言，大型语言模型（LLM）可以成为一个非常有效的工具；然而，它也可能误导那些缺乏足够学科背景的学生。跨越不同学期的两个学生群体的结果表明，P2P教学框架在提升人工智能素养和工程推理能力方面具有教学有效性。