The Relationship between Personal Status and Artificial Intelligence Literacy of Science and Technology Teachers: A Case Study of Surasakmontree School
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Abstract
This Article aimed to study 1) study the artificial intelligence literacy level of science and technology teachers at Surasak Montri School, 2) compare their AI literacy levels across personal characteristics (including generation, academic standing, and teaching subjects), and 3) examine the relationship between personal characteristics and AI literacy levels. The study employed a quantitative research design based on Long and Magerko's (2020) framework. The research site was Surasakmontree School, with a population of 32 science and technology teachers selected through purposive sampling. The research instrument was an AI literacy assessment adapted from Hornberger et al. (2023). Data analysis utilized descriptive statistics, including frequency, percentage, mean, and standard deviation, and inferential statistics using Pearson's Correlation Coefficient. The findings revealed that 1) teachers demonstrated a moderate level of AI literacy (mean = 38.2), 2) computer science teachers achieved the highest mean score (45.5), and 3) teaching subjects showed a statistically significant high positive correlation with AI literacy (r = .516, p < .01). The knowledge gained from this research can be applied to develop specialized AI training curricula for teachers across different subjects.
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