A Survey Study on AI Literacy of Nakhon Sawan Rajabhat University’s Digital Technology Teacher Students in Thailand

In the realm of education, the integration of AI literacy into computer science teaching is becoming increasingly crucial (Walsh et al., 2023; Voulgari et al., 2022; Velander et al., 2023). Teachers play a pivotal role in bridging the gap between research and practical knowledge transfer of AI-related skills, necessitating a solid foundation in AI-related technological, pedagogical, and content knowledge (TPACK) (Velander et al., 2023). As AI systems permeate various aspects of society, including education, teachers must adapt and develop competencies in AI to effectively impart these skills to students (Kreinsen & Schulz, 2023). The incorporation of AI ethics into the curriculum requires teachers to navigate complex issues such as biases related to race, gender, and social class, challenging both computer science and humanities educators to step out of their comfort zones and collaborate to provide high-quality instruction (Walsh et al., 2023). By leveraging their expertise in different domains and receiving support from research teams, teachers can create engaging learning experiences that prepare students for the ethical and technical challenges posed by AI systems (Walsh et al., 2023).This article aimed to study the AI literacy level of teacher students major in digital technology who study at Nakhon Sawan Rajabhat University in Thailand. There were 98 students responded the AI literacy questionnaire which contained of 4 factors (Knowledge and Use of AI 24 questions, Creation of AI 3 questions, AI Self-Efficacy 6 questions, and AI Self-Competency 7 questions). The results showed that, 1) there were no statistically significant differences in gender among Knowledge and Use, AI Self-Efficacy, and AI Self-Competency while has statistically highly significant as P < 0.05 in Creation of AI factor, 2) there were no statistically significant different in level of study and use of time used of computer among AI literacy factors, and 3) there was relationship between AI literacy factors with statistically highly significant as P < 0.01.