Abstract
Prompt engineering involves the strategic formulation of clear and structured instructions to optimise interactions with generative AI. As AI technologies become increasingly integrated into educational practices, it is essential for educators to understand how to effectively guide these systems to enhance teaching and learning outcomes for undergraduate students. This study proposes a comprehensive theoretical framework that integrates established models, focusing on equipping educators with the necessary skills to assess and improve their prompt engineering practices. By examining the principles underlying these frameworks, the proposed model emphasises key competencies such as clarity, specificity, and iterative refinement. Through the development of this framework, the study seeks to provide insights into how prompt engineering can be systematically integrated into undergraduate education curricula.
Presenters
William Ko Wai TangActing Head of Education and Assistant Professor, Education, Hong Kong Metropolitan University, Hong Kong
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Prompt Engineering, Undergraduate, Generative AI, Theoretical Framework
