https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/516 Sat, 04 Jul 2026 18:58:57 GMT 2026-07-04T18:58:57Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212774 Title: Developing pre-service English teachers’ critical and ethical AI literacy in writing with generative AI Authors: Kim, Sung-Yeon Abstract: This mixed-methods study explored the effects of AI literacy (AIL) training on pre-service English teachers’ writing proficiency and Al literacy development. Employing a quasi-experimental design (n = 60), the study compared an AIL training group (G1), a just-writing group (G2), and a control group (G3). All groups completed surveys measuring critical and ethical AI literacy, while G1 and G2 (n = 43) also completed writing tests. Qualitative data were collected from G1 through reflection journals and follow-up interviews (n = 7). A one-way analysis of covariance (ANCOVA), using pretest scores as a covariate to control for cohort effects, revealed that the AIL group significantly outperformed the just-writing group in post-intervention writing proficiency ( F (1, 40) = 13.29, p < .001). While self-reported surveys showed no significant shifts in critical or ethical AI competence ( p > .05), qualitative data substantiated the behavioral manifestation of AI competence. Students in the AIL group demonstrated critical competence through behaviors such as cross-checking AI-generated sources, and limiting AI use to brainstorming after experiencing hallucinations. Based on these findings, the study proposes two conceptual frameworks. The TPEC (Technological, Pedagogical, Ethical, and Critical) framework is presented as a theoretical expansion of existing AI integration models for teacher education. The TDCE model (comprising Technological, Dialogical, Critical, and Ethical dimensions) outlines a developmental trajectory of AI literacy from awareness to competence across the four dimensions. These models suggest that structured AIL training supports the responsible integration of generative AI into writing instruction, even when changes in attitudes and competencies are not immediately captured by self-reported questionnaires. Wed, 01 Jul 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212774 2026-07-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217601 Title: Exploring the potential of dynamic time warping algorithms for automated intonation evaluation in EFL learners Authors: 원용국 Fri, 22 May 2026 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/217601 2026-05-22T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/207387 Title: The impact of self-revision, machine translation, and ChatGPT on L2 writing: Raters’ assessments, linguistic complexity, and error correction Authors: Kim, Minjoo; Chon, Yuah V. Abstract: This study explores how learners in a South Korean high school English as a Foreign Language (EFL) context can effectively use neural machine translation (MT) and ChatGPT to enhance their L2 writing. While recent AI tools offer significant potential for supporting human writing feedback, a comparative analysis of how these tools impact writing outcomes—compared to when L2 writers independently proofread and revise their writing—has not been fully examined. To address this gap, a controlled experiment was conducted using three distinct proofreading interventions—self-proofreading (SP), MT-assisted proofreading (MAP), and ChatGPT-assisted proofreading (CAP). Learners were encouraged to first compose their texts in their L2 and then use either MT through inverse translation or ChatGPT through a structured proofreading process. The findings revealed that learners using MAP and CAP demonstrated substantial improvements in overall writing quality compared to those relying solely on SP. CAP users, in particular, produced longer texts, exhibited greater lexical diversity, and constructed more complex sentences, although this was accompanied by reduced verb cohesion. Both MAP and CAP significantly reduced grammatical errors, but did not affect prepositional errors. These findings provide practical recommendations for integrating MT and ChatGPT into L2 writing pedagogy. Tue, 01 Jul 2025 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/207387 2025-07-01T00:00:00Z https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213404 Title: 공통영어2 Authors: 김성연 Sat, 01 Mar 2025 00:00:00 GMT https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/213404 2025-03-01T00:00:00Z