As higher education digitizes, large‑language‑model(LLM) writing aids and automated essay scoring can lighten instructors’ workloads while giving students instant, personalized feedback. Their value, however, remains unclear in humanities courses demanding deep critical inquiry, such as Japanese culture and literature. This study outlines a three‑semester mixed‑methods design integrating LLM tools into an authentic course and testing (1) gains in students’ writing quality, (2) the reliability and acceptance of AI scoring versus human raters, and (3) links between AI‑use intensity and achievement. Roughly 700 undergraduates will be assigned to randomized treatment or control sections each term. Data sources include pre‑/post‑essay scores on a 10‑item rubric, AI‑human concordance indices, Likert surveys on usability and fairness, granular usage logs, and end‑of‑term focus‑group interviews. Interviews capture learners’ experiences, strategies, and concerns about AI adoption, and logs track function choice and revision cycles. Hierarchical linear models, gain‑score ANCOVAs, Cohen’s κ, Bland–Altman plots, and structural equation modeling will analyze nested effects, scoring validity, and attitudinal constructs. Expected results include usage‑dependent improvements in writing, strong AI‑human agreement when explanations are transparent, and positive achievement gains for students who employ AI strategically. By detailing instruments, procedures, and analyses, the study offers a replicable template for evaluating AI as a cognitive partner and instructional aid in humanities settings, informing sustainable AI‑ enhanced teaching, assessment, and curriculum design.
한국일본언어문화학회 [Japanese Language & Culture Association of Korea]
설립연도
2001
분야
인문학>일본어와문학
소개
본 학회는 일본어학 및 일본문학은 물론, 일본의 정치, 경제, 문화, 사회 등의 일본학 전반에 걸친 연구 및 일본의 언어, 문화를 매체로 한 한국과의 비교 연구를 대상으로 하고 있다. 본 학회는 회원들에게 연구 발표 및 정보 교환의 기회를 부여하고 나아가 한국에서의 바람직한 일본 연구 자세를 확립하는 것을 주된 목표로 하고 있다.