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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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This study develops ACME, a comprehensive AI-based career support platform built through vibe coding. The ACME platform integrates four interconnected modules: Assistant AI, which supports preparation for the Secondary School English Teacher Recruitment Exams using a dataset of 806 English test items from 2002–2025 along with 365 documented success narratives systematically collected from Naver blogs (n = 167), online teacher certification communities (n = 101), university recruitment archives (n = 34), and YouTube interviews/vlogs (n = 63); Consultant AI, facilitating non-teaching career exploration through employment database integration and labor market intelligence; Mediator AI, coordinating institutional resources with individual career planning; and Educator AI, providing teaching methodology support via curated educational technology tools. Drawing on three primary data sources: (1) institutional records of career and extracurricular program participation (2022–2024), (2) a pre-survey of 54 English education majors, and (3) a post-pilot usability test involving 36 students—the study examined English education majors’ career identities, advising needs, and platform effectiveness. Findings from a preliminary needs assessment (n = 54) revealed significant career development challenges: while students demonstrated strong major satisfaction (55.6%) and interest (61.1%), only 44.4% expressed confidence in their career readiness confidence. Career aspirations fluctuated dramatically across academic years (first/second year: 76.9%/70.0%; third year: 33.3%; fourth year: 66.7%), reflecting students’ evolving awareness of teaching market realities. Demand for career counseling services tripled between 2022-2024 (20 to 61 annual participants), yet specialized programming for non-teaching careers markedly declined. A pilot evaluation (n = 36) confirmed platform utility, with highest ratings for examination preparation (M = 4.44) and teaching methodology resources (M = 4.36). Qualitative analysis revealed student requests for enhanced functionality including automated error-pattern detection, expanded non-teaching career outcome databases, community-based peer matching, and career-aligned activity recommendations. The study validates vibe coding as an effective method for enabling sustainable, low-cost platform development, demonstrating that regional universities can autonomously create department-level, discipline-specific AI systems supporting personalized, data-driven career guidance.

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기사명 저자명 페이지 원문 목차
다차원적 메타버스 플랫폼 기반 영어 발표 수업 = Multi-dimensional metaverse platform-based English presentation : learner factors and learning outcomes : 학습자 변인과 학습성과 하명정 p. 1-24
Analyzing linguistic gains and interaction patterns in ChatGPT-assisted L2 writing revision : a case study of adult EFL learners Myunghwan Hwang, Hee-Kyung Lee p. 25-52
Engagement with language through ChatGPT use : a systematic review Robert C. Kerr p. 53-72
Developing instructional materials for the integration of educational technology in English teaching Mee-Jee Kim p. 73-94
Personalized learning through AI : from rule-based systems to AI mentors Sangmin-Michelle Lee, Junseong Bang p. 95-114
바이브코딩을 통한 ACME 플랫폼 개발 = Integrating vibe coding into ACME : an AI-driven career, learning, and counseling platform for English education majors : 영어교육과 학생을 위한 인공지능 기반 진로·학습·상담 지원모델 연구 이혜진, 이도원 p. 115-142
영어 AI 디지털교과서에 대한 고등학생의 인식 = High school students' perceptions of English AI digital textbooks : a comparison based on English learning achievement : 영어학습 성취도에 따른 비교 이정은, 성은경 p. 143-168
패들렛(Padlet) 기반 과정 중심 쓰기 수업이 EFL 대학생의 쓰기 동기와 전략 사용에 미치는 영향 = The effects of Padlet-based process writing instruction on EFL university students' writing motivation and strategy use 이동주 p. 169-192
AI기반 영어 읽기 문항 자동 출제 방안 탐색 = Exploring AI-based automatic item generation for English reading comprehension 이문정, 이용상, 홍익현 p. 193-212
AIDT의 한국어 교육 적용 가능성 탐색 = Exploring the applicability of AIDT to Korean language education : a case review of Korean for All and recommendations : ‘모두의 한국어’의 사례 검토 및 제언 이대현, 한혜민 p. 213-233
ChatGPT의 평가와 피드백에 나타난 프레이밍 효과 = Framing effects on ChatGPT's evaluation and feedback : an exploratory mixed-methods study : 탐색적 혼합 연구 오현주 p. 234-255

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