KNOWLEDGE AND UNDERSTANDING OF PROMPT ENGINEERING PRINCIPLES FOR KU GENAI PLATFORM AMONG UNDERGRADUATE STUDENTS AT KASETSART UNIVERSITY

Abstract

This study aimed to: 1) examine the level of knowledge and understanding of prompt engineering principles for KU GenAI Platform among undergraduate students at Kasetsart University; 2) compare knowledge and understanding based on personal characteristics; and 3) examine the association between regularly used media and the level of knowledge and understanding. The sample consisted of 530 undergraduate students enrolled in the second semester of the 2025 academic year at Kasetsart University. Data were collected using a questionnaire. Statistical techniques employed for data analysis included frequency, percentage, mean, standard deviation, Mann-Whitney U test, Kruskal-Wallis H test with Bonferroni-adjusted pairwise comparisons, Chi-square test, and Cramér’s V, with statistical significance set at the .05 level. The findings revealed that undergraduate students demonstrated a high level of knowledge and understanding of prompt engineering principles for the KU GenAI Platform (Mean = .85, S.D. = .20). All dimensions were also reported at a high level, including prompt refinement (Mean = .87, S.D. = .22), prompt clarity (Mean = .87, S.D. = .23), prompt constraints (Mean = .86, S.D. = .22), and information security (Mean = .80, S.D. = .29). Hypothesis testing indicated that students of different sexes and faculties exhibited significantly different levels of knowledge and understanding, whereas no significant difference was found among students of different years of study. In addition, regularly used media showed a weak association with the level of knowledge and understanding (Cramér’s V = .13).

Keywords: Prompt Engineering, KU GenAI Platform, Generative Artificial Intelligence, AI Literacy, Higher Education