Leveraging Prompting Guides as Worked Examples for Advanced Prompt Engineering Strategies

Artificial intelligence systems, particularly those based on large language models, are increasingly prevalent in personal and professional settings, making the skill of prompt engineering—formulating effective AI inputs—vital. This paper explores the effects and how to enhance students' prompt engineering skills based on a multi-study design. Our first study confirmed the baseline hypothesis that prompt engineering can predict AI output quality, framing it as a critical skill. We then investigated whether instructional designs (worked examples & instructions), could develop prompting skills. Using worked examples, the second study tested the effectiveness of instructional materials on enhancing prompting skills. The experiment involved 245 students who demonstrated that a brief exposure to a worked example-based prompting guide significantly improved their ability to deploy targeted prompting strategies. These findings suggest that integrating worked examples into curricula could effectively equip students with essential prompting skills, offering both theoretical and practical implications for AI education.

Publication Details

Journal
Journal of the Association for Information Systems
Published
2024-12-15
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging Prompting Guides as Worked Examples for Advanced Prompt Engineering Strategies

Journal of the Association for Information Systems
Intelligent Tutoring Systems and Adaptive Learning
article

Leveraging Prompting Guides as Worked Examples for Advanced Prompt Engineering Strategies

article en

Abstract

Artificial intelligence systems, particularly those based on large language models, are increasingly prevalent in personal and professional settings, making the skill of prompt engineering—formulating effective AI inputs—vital. This paper explores the effects and how to enhance students' prompt engineering skills based on a multi-study design. Our first study confirmed the baseline hypothesis that prompt engineering can predict AI output quality, framing it as a critical skill. We then investigated whether instructional designs (worked examples & instructions), could develop prompting skills. Using worked examples, the second study tested the effectiveness of instructional materials on enhancing prompting skills. The experiment involved 245 students who demonstrated that a brief exposure to a worked example-based prompting guide significantly improved their ability to deploy targeted prompting strategies. These findings suggest that integrating worked examples into curricula could effectively equip students with essential prompting skills, offering both theoretical and practical implications for AI education.

Journal of the Association for Information Systems
Quality Education
Openalex Percentile: Top 54%
Intelligent Tutoring Systems and Adaptive Learning
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