Prompt-to-Primal Teaching

TL;DR

Prompt-to-Primal Teaching is an innovative instructional method that integrates AI prompts with first-principles reasoning, enhancing engineering understanding and AI literacy through guided student inquiry and critical evaluation.

Contribution

This paper introduces the novel Prompt-to-Primal Teaching framework, combining AI-driven prompts with foundational reasoning to improve engineering education.

Findings

Enhanced student engagement and understanding of core principles.

Improved critical evaluation skills of AI outputs.

Positive pedagogical outcomes across multiple student cohorts.

Abstract

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.