English pre-service teachers with prior AI learning experience demonstrate significantly higher AI literacy levels and stronger interest in AI education compared to their counterparts without such experience.

Objective: The main goal of this study was to explore the AI literacy of English pre-service teachers (PSTs) in South Korea by investigating their interest in AI, recognition of its necessity, knowledge and attitudes toward AI, difficulties in learning AI, prior AI learning experiences, and the types of AI education they received. The researchers aimed to understand what factors influence English PSTs' AI literacy and their perceptions of AI integration in English education.

Methods: The researchers conducted an online survey using a convenient sampling method with 75 English PSTs from a private college's Department of English Education in central South Korea. The study employed a modified version of Kim et al.'s (2022) survey instrument, consisting of demographic questions and 24 Likert-scale items (ranging from 1-5) measuring five key variables: interest in AI (3 questions), necessity of AI (4 questions), knowledge of AI (3 questions), attitudes toward AI (6 questions), and difficulty in learning AI (8 questions). The survey demonstrated good reliability with Cronbach's α ranging from .656 to .932. Data analysis was performed using SPSS 27.0, including descriptive statistics and independent sample t-tests or one-way ANOVA to examine differences based on prior AI learning experience and type of AI education.

Key Findings:

• English PSTs showed considerable interest in AI (mean scores above 3.80) and expressed strong desire to gain AI knowledge and skills.
• PSTs with prior AI learning experience (42.7% of participants) demonstrated significantly higher interest in AI applications, core knowledge acquisition, and specialized AI education compared to those without experience.
• Regarding necessity, PSTs with prior AI experience showed significantly stronger beliefs that their department should teach AI usage, introduce dedicated AI courses, and provide coding skills training.
• In terms of knowledge, PSTs with AI experience were significantly more familiar with applying AI in English education (p<.001), though overall AI knowledge levels remained moderate across all participants.
• The most significant learning difficulties identified were lack of coding knowledge (M=4.22), math knowledge (M=3.70), and learning opportunities (M=3.61).
• PSTs without AI experience reported significantly greater difficulty due to lack of computer knowledge compared to their experienced counterparts.
• Notably, while prior AI learning experience influenced various aspects of AI literacy, the specific type of AI education (K-12 software education, college courses, or short-term training) showed no significant impact on any measured variables.

Implications: These findings highlight the critical importance of providing AI education opportunities for English PSTs, as over half (57.3%) had no prior AI learning experience. The study demonstrates that exposure to AI education, regardless of its format, significantly enhances PSTs' readiness to integrate AI technologies into language teaching. The research supports the development of dedicated AI courses within English education programs and emphasizes the need for comprehensive AI literacy training that addresses both technical skills and pedagogical applications. The findings suggest that teacher education institutions should prioritize creating structured AI learning experiences to prepare future English educators for technology-enhanced teaching environments.

Limitations: The study has several notable limitations. The sample size was relatively small (75 participants) and drawn from a single institution using convenient sampling, which limits generalizability. The research focused exclusively on English PSTs from one private college in central South Korea, potentially restricting applicability to other contexts or subject areas. The cross-sectional design prevents understanding of how AI literacy develops over time. Additionally, the study relied on self-reported measures, which may introduce response bias, and the survey instrument, while reliable, may not capture all dimensions of AI literacy comprehensively.

Future Directions: The researchers recommend several areas for future investigation. Longitudinal studies should track the development of AI literacy over time and assess the long-term effectiveness of different AI education approaches. Research should expand to include larger, more diverse samples across multiple institutions and geographic regions. Future studies should explore the effectiveness of specific AI education models, such as project-based learning, blended learning approaches, and collaborative AI workshops. Additionally, research should investigate optimal strategies for integrating AI education into subject-specific teacher training programs and examine the practical application of AI literacy skills in real classroom settings through teaching practicum opportunities.

Title and Authors: "An Exploratory Study on English Pre-service Teachers' AI Literacy" by Hyoung Sook Cho and Yong Jik Lee.

Published On: June 2025 (received January 15, 2025; revised March 8, 2025; accepted March 18, 2025)

Published By: The Journal of Educational Development, Educational Research Institute (Volume 45, Number 1, pages 301-323)