English education major’s practices and perceptions of AI use: Opportunities and ethical challenges
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This study examined pre-service English teachers’ perceptions, applications, and ethical orientations toward AI tools in English education. A mixed-methods survey was administered to 59undergraduates, combining a 40-item quantitative scale with open-ended responses, and analyzed using descriptive statistics, group comparisons, and thematic analysis. Findings revealed three major patterns. First, participants perceived AI as useful for reducing burden, enhancing efficiency, and supporting lesson preparation, but their actual applications remained limited to basic functions, highlighting a perception–practice gap. Second, usage frequency in the data showed clear threshold effects: moderate use supported efficiency gains, while only high-frequency engagement enabled recognition of creative or higher-order benefits. Duration of use, by contrast, had weaker and less consistent effects, underscoring that intensity of engagement matters more than exposure length. Third, concerns about overreliance, creativity decline, fairness, plagiarism, and reliability were stable across groups, reflecting baseline normative orientations. Interpreted through the Technology Acceptance Model (Davis, 1989), students’ emphasis on efficiency and burden reduction aligned with perceived usefulness. However, reliability concerns fall outside core Technology Acceptance Model (TAM)constructs, pointing to the need for complementary theoretical perspectives. Beyond TAM, the findings highlight ethical and normative dimensions as essential to AI adoption in teacher education. The study underscores the dual task of fostering meaningful engagement with AI while safeguarding integrity—an issue of particular urgency in Korea, where AI-driven digital textbooks will be introduced nationwide in 2025.
Abstract
This study examined pre-service English teachers’ perceptions, applications, and ethical orientations toward AI tools in English education. A mixed-methods survey was administered to 59undergraduates, combining a 40-item quantitative scale with open-ended responses, and analyzed using descriptive statistics, group comparisons, and thematic analysis. Findings revealed three major patterns. First, participants perceived AI as useful for reducing burden, enhancing efficiency, and supporting lesson preparation, but their actual applications remained limited to basic functions, highlighting a perception–practice gap. Second, usage frequency in the data showed clear threshold effects: moderate use supported efficiency gains, while only high-frequency engagement enabled recognition of creative or higher-order benefits. Duration of use, by contrast, had weaker and less consistent effects, underscoring that intensity of engagement matters more than exposure length. Third, concerns about overreliance, creativity decline, fairness, plagiarism, and reliability were stable across groups, reflecting baseline normative orientations. Interpreted through the Technology Acceptance Model (Davis, 1989), students’ emphasis on efficiency and burden reduction aligned with perceived usefulness. However, reliability concerns fall outside core Technology Acceptance Model (TAM)constructs, pointing to the need for complementary theoretical perspectives. Beyond TAM, the findings highlight ethical and normative dimensions as essential to AI adoption in teacher education. The study underscores the dual task of fostering meaningful engagement with AI while safeguarding integrity—an issue of particular urgency in Korea, where AI-driven digital textbooks will be introduced nationwide in 2025.