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Prompt engineering to improve the performance of teaching and learning materials Recommendation of Generative Artificial Intelligence

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2023, 28(8), pp.195-204
  • DOI : 10.9708/jksci.2023.28.08.195
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 21, 2023
  • Accepted : August 21, 2023
  • Published : August 31, 2023

Soohwan Lee 1 Song Kisang 2

1오현초등학교
2한국교원대학교

Accredited

ABSTRACT

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.

Citation status

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