Paper Number
1322
Paper Type
Short Paper
Abstract
This paper explores the evolving field of prompt engineering in Artificial Intelligence (AI), with a focus on Large Language Models (LLMs). As LLMs exhibit remarkable potential in various educational domains, their effective use requires adept prompt engineering skills. We introduce a skill-based approach to prompt engineering and explicitly investigate the impact of using worked examples to facilitate prompt engineering skills among students interacting with LLMs. We propose hypotheses linking prompt engineering, worked examples, and perceived anthropomorphism to the quality of LLM output. Our initial findings support the critical relationship between proficient prompt engineering and the resulting output quality of LLMs. Subsequent phases will further explore the role of worked examples in prompt engineering, aiming to provide practical recommendations for educational improvement and industry application. Additionally, this research aims to shed light on the responsible utilization of LLMs in education and contribute insights to educational practice, research, and organizational development.
Recommended Citation
Tolzin, Antonia; Knoth, Nils; and Janson, Andreas, "Worked Examples to Facilitate the Development of Prompt Engineering Skills" (2024). ECIS 2024 Proceedings. 10.
https://aisel.aisnet.org/ecis2024/track13_learning_teach/track13_learning_teach/10
Worked Examples to Facilitate the Development of Prompt Engineering Skills
This paper explores the evolving field of prompt engineering in Artificial Intelligence (AI), with a focus on Large Language Models (LLMs). As LLMs exhibit remarkable potential in various educational domains, their effective use requires adept prompt engineering skills. We introduce a skill-based approach to prompt engineering and explicitly investigate the impact of using worked examples to facilitate prompt engineering skills among students interacting with LLMs. We propose hypotheses linking prompt engineering, worked examples, and perceived anthropomorphism to the quality of LLM output. Our initial findings support the critical relationship between proficient prompt engineering and the resulting output quality of LLMs. Subsequent phases will further explore the role of worked examples in prompt engineering, aiming to provide practical recommendations for educational improvement and industry application. Additionally, this research aims to shed light on the responsible utilization of LLMs in education and contribute insights to educational practice, research, and organizational development.
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