Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study
David James Woo, Deliang Wang, Tim Yung, and Kai Guo

TL;DR
This study demonstrates that a targeted prompt engineering workshop significantly improves undergraduate students' AI self-efficacy, AI knowledge, and prompt creation skills, highlighting the importance of structured AI literacy education.
Contribution
It introduces a novel prompt engineering intervention for undergraduates and provides evidence of its effectiveness in enhancing AI-related skills and confidence.
Findings
Students' AI self-efficacy increased post-intervention.
Students showed improved understanding of AI concepts.
Prompt engineering skills were significantly enhanced.
Abstract
Prompt engineering is critical for effective interaction with large language models (LLMs) such as ChatGPT. However, efforts to teach this skill to students have been limited. This study designed and implemented a prompt engineering intervention, examining its influence on undergraduate students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts. The intervention involved 27 students who participated in a 100-minute workshop conducted during their history course at a university in Hong Kong. During the workshop, students were introduced to prompt engineering strategies, which they applied to plan the course's final essay task. Multiple data sources were collected, including students' responses to pre- and post-workshop questionnaires, pre- and post-workshop prompt libraries, and written reflections. The study's findings revealed that students demonstrated a…
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Taxonomy
TopicsDiverse Interdisciplinary Research Innovations · Engineering Education and Technology · Technology and Data Analysis
