This study aims to compare four common types of feedback used in university English presentation activities: artificial intelligence (AI), professor, peer, and self-feedback. A total of 38 first-year students enrolled in a first-year English course at a Korean university participated in this study by recording and evaluating practice videos to provide feedback to help improve their final presentations. Each of the two practice videos received evaluation scores and written comments from multiple sources, including AI tools, the professor, their peers, and themselves. To examine students’ perceptions of these feedback sources, data were collected through Likert-scale survey items measuring perceived effectiveness and preference, as well as open-ended responses explaining students’ choices. Data analysis included descriptive statistics for the survey responses and content analysis for the qualitative comments. The research findings are as follows. Firstly, professor feedback was perceived as the most effective source for improving presentation performance. Secondly, peer feedback was viewed as helpful for providing additional perspectives on presentation quality. Thirdly, AI and self-feedback were generally perceived as less reliable than human feedback. These findings suggest that combining multiple feedback sources may support speaking development in university English presentation tasks, with pedagogical implications and directions for future research discussed.

As artificial intelligence and digital technologies are widely used in writing instruction, classrooms are adopting technology-supported feedback. This study investigates changes in learners’ self-regulated learning in a ChatGPT-based feedback English writing course. The research question for this study was: How does self-regulated learning change across motivational, cognitive, and behavioral domains before and after the course? The study was conducted in the English writing classes during the fall semester of 2025. A total of 48 students participated over a 15-week period. Data were collected through preand post questionnaires, in-depth interviews, and classroom observations. Quantitative data were analyzed using the Wilcoxon matched pairs signed-rank test while qualitative data were analyzed to support and explain the quantitative results. The major finding was that statistically significant changes were observed only in learners’ self-efficacy within the motivational domain over the course. The finding suggests that ChatGPT-based feedback is effective in supporting learners’ motivational regulation, particularly self-efficacy. However, to promote broader development of self-regulated learning, AI-based feedback may need to be accompanied by instructional support that guides learners’ monitoring, decision-making, and sustained engagement.

In modern architecture acoustic design, the psychological feedback from the audience has been important as a traditional acoustic parameter. Therefore, this study analyzes an acoustic psychological feedback from audience through an exhibition performance. A feedback from 660 audiences for eight different categories was evaluated. For the result, above 5 out 7 in entire indicators which indicates that the audience was satisfied with the hall. There was a remarkable deviation depending on floors and age groups. 2nd floor provides higher points in most indicators with 5.75 for an envelopment and 5.61 for a balance, whereas 1st floor provides the least with 5.16 for loudness and 5.17 for envelopment. Moreover, audience in their 30s evaluated acoustics more critically, whereas audience in their 50s rated more positively.

This study examines the developmental trajectory of feedback research in Korean EFL writing from the early 1990s to 2025 through a qualitative systematic synthesis of KCIindexed journal articles. Drawing on major learning theories and educational reforms, the study identifies four stages that reflect shifts in curriculum policy, assessment practices, and the gradual incorporation of digital and AI-based tools. Early work was characterized by teacher-centered and form-focused corrective feedback, followed by comparative studies of feedback types influenced by sociocultural theory and formative assessment perspectives. Recent research has increasingly addressed learners’ metacognition, self-regulation, feedback uptake, and AI-mediated practices. Despite these broader theoretical orientations, the literature remains dominated by short-term interventions and perception-based studies, with limited evidence on sustained writing development in classroom settings. The findings indicate a gap between theoretical advancement and instructional practice in Korean EFL writing. This study calls for longitudinal, classroom-based, and mixed-method research that examines how teacher-, peer-, and AI-mediated feedback can be integrated within context-sensitive instructional models.

This study examines two AI-driven written feedback tools—ChatGPT and AI Digital Textbooks (AIDT)—by comparing their effects on Korean middle school students’ English writing performance and perceptions. Over six weeks, fifty seventh graders completed six expository writing tasks and received feedback from either ChatGPT or AIDT. Pre- and post-tests and questionnaires assessed changes in writing performance and affective factors, including motivation, self-efficacy, metacognition, and anxiety. Results showed significant improvement in both groups with large effect sizes, although overall post-test scores did not differ. Subcomponent analyses revealed that ChatGPT better supported content and idea development, while AIDT was stronger in grammar and cohesion. ChatGPT users also reported higher motivation, self-efficacy, and perceived usefulness. Overall, both tools supported L2 writing, but with distinct pedagogical strengths: AIDT promoted accuracy through structured, rule-based feedback, whereas ChatGPT provided adaptive, dialogic feedback encouraging higherorder thinking. These findings suggest that tool selection should align with learners’ proficiency levels and instructional goals across different stages of writing.

Background: Despite various treatment methods, many functional ankle instability (FAI) patients continue to experience functional deficits. Objectives: To investigate the effect of additional auditory feedback on balance and ankle function in task-oriented balance training for FAI patients. Design: Randomized controlled trial design. Methods: Forty FAI patients were randomly assigned to the auditory feedback task-oriented balance training (ATBT) group (n=20) and the task-oriented balance training (TBT) group (n=20). The training program was implemented for 4 weeks, 3 days a week. Results: After training, the COG movement area, speed, and distance significantly decreased in both the ATBT and TBT groups (P<.05). Additionally, the COG movement speed and distance in the ATBT group were significantly decreased compared to the TBT group (P<.05). The 6-meter crossover hop test time decreased in both the ATBT and TBT groups (P<.05). The ATBT group exhibited a significantly decreased 6-meter crossover hop test time compared to the TBT group (P<.05). Conclusion: Both ATBT and TBT enhanced balance and ankle function in FAI patients. ATBT was more effective in improving balance and ankle function than TBT.

This qualitative study explores how fifth-grade elementary school students experience AI-based feedback in English writing. Two students participated in writing activities and received feedback from ChatGPT. Data were collected from observation logs, drafts, reflection journals, and interviews over several weeks. The analysis focused on participants’ perceptions, cognitive/meta-cognitive and affective responses, and the impact of feedback on revision. Results indicate AI-based feedback is helpful, clear, and supports self-directed learning, increasing motivation and confidence. Effective classroom use requires instructional sequences integrating AI feedback with teacher facilitation, including drafting, feedback reception, revision, and reflection stages. Teacher training should prepare educators to manage AI tools effectively and foster students’ self-regulation. Teacher-led research and collaboration can enhance AI feedback integration, ensuring meaningful learning experiences for young EFL learners. These findings provide foundational insights for developing personalized feedback systems and instructional models combining technological innovation and pedagogy, supporting young learners’ growth as confident and autonomous writers.

This study investigates how artificial intelligence (AI)-generated and instructor-provided feedback interact within process-based, collaborative EFL writing to influence learners’ affective engagement, motivation, self-efficacy, and attitude toward writing. Semistructured group interviews with nine pre-service English teachers were analyzed using NVivo 14. Among the three categories, attitude, encompassing both positive and negative factors, was the most frequently observed, followed by self-efficacy and motivation. Positive attitude was linked to the convenience, immediacy, and personalization of AI feedback, and to the collaborative course design that supported iterative revision and peer learning. Negative attitude reflected concerns about AI overreliance, inaccuracies, and limited contextual appropriateness, particularly in ESPfocused writing. Regarding self-efficacy, instructor feedback provided deep, contextaware guidance, whereas AI feedback offered rapid, mechanical accuracy and motivational support. Confidence improved through safer drafting, metacognitive insight, and gradual mastery of AI utilization skills. Motivation was reinforced by the process-oriented structure, targeted instructor feedback, collaborative activities, and peer support that fostered engagement and improvement.

This study investigates how AI feedback learning activities in English writing classes affect university students’ English achievement and writing motivation. It examines both cognitive and affective outcomes in EFL contexts using generative AI tools such as ChatGPT. A total of 244 first-year university students completed four writing tasks incorporating AI feedback during the revision process. Pre- and post-tests were conducted along with the Oxford Placement Test (Syndicate, 2001) and a modified version of Payne’s (2012) Academic Writing Motivation Questionnaire. Quantitative analyses revealed significant improvement in English achievement and three motivational subcomponents—enjoyment, recognition, and effort—while instrumentality showed no significant change. Qualitative analysis of open-ended questionnaire responses indicated that students valued AI feedback for error detection, coherence, and self-directed learning, yet recognized limitations such as unnatural expressions, misinterpretations, and overreliance on AI. The findings highlight the importance of AI literacy and propose a hybrid feedback model integrating AI and teacher guidance to enhance learner autonomy and writing competence.

본 연구는 한국의 제품안전인증 제도와 제조물책임법(PL)상 결함 인정 기준을 미국, EU, 일본의 제도와 비교·분석함으로써, 사전 예방적 규제와 사후적 책임체계 간의 구조적 간 극을 규명하였다. 제품안전인증 제도는 최소한의 안전 수준 확보를 위한 사전적 규제수단 으로 작동하는 반면, 제조물책임 제도는 소비자의 합리적 기대 수준을 기준으로 한 사후적 책임체계로 기능한다. 이러한 제도적 이원성은 소비자 보호와 기업 규제이행 간의 균형을 위한 필연적 구조로 이해될 수 있으나, 양 제도 간 연계가 미흡할 경우 소비자 피해 및 기업은 규제와 소송 대응에서 불확실성과 이중 부담에 직면하게 된다. 특히 한국의 경우, 제조물책임 판결 결과가 안전기준의 개정으로 환류되는 절차가 미흡하여 법적 판단과 행정적 규제 간의 정보 연계가 부족한 것으로 나타났다. 이에 본 연구는 판례 기반 결함정 보의 체계적 환류 절차 구축의 필요성을 제시하였다. 구체적으로는 (1) 결함 및 판례 데이 터를 통합적으로 수집·관리하는 결함정보 플랫폼 구축, (2) 인증기관 간 정보공유 네트워 크 강화, (3) 기업의 ESG 기반 위험관리체계 고도화를 핵심 과제로 도출하였다. 연구의 주요 시사점으로는 첫째, 위험도 기반의 인증기준 상향조정을 통해 기술 변화와 시장 위험 을 반영해야 하며, 둘째, 리콜 제도의 실효성 강화를 통해 결함 대응의 투명성과 신속성을 확보해야 한다. 셋째, 미국(CPSC·NHTSA), EU(RAPEX), 일본(소비자청) 등 해외 주요국 의 정보공유 및 환류체계를 벤치마킹하여 국제적 표준화 기반을 확립할 필요가 있다. 넷 째, 기업은 ESG 경영 관점에서 자발적 안전기준 강화 및 리스크 커뮤니케이션 체계 구축 을 통해 이해관계자 신뢰를 제고하고, 규제 리스크를 선제적으로 관리해야 한다.

본 연구는 조직 구성원의 정서인식이 인지적 정서조절을 매개로 부정적 피드 백 수용에 미치는 영향을 살펴보고, 이 과정에서 그릿(Grit)이 조절 역할을 하는 지를 검증하고자 하였다. 이를 위해 국내 기업에 재직 중인 비관리자급 구성원 174명을 대상으로 설문을 실시하고, 유효 응답 158부를 최종 분석에 활용하였 다. 수집된 자료는 PROCESS macro를 통해 분석하였다. 분석 결과, 정서인식은 인지적 정서조절을 매개로 부정적 피드백 수용에 유의한 영향을 미쳤으며, 이 경로에서 그릿의 조절 효과가 확인되었다. 주요 변수 간 상관관계는 모두 유의 하였고, 간접효과에 대한 그릿의 조절된 매개효과도 유의하였다. 이는 조직 내 정서적 자원과 개인 특성이 상호작용하여 피드백 수용에 영향을 미친다는 점을 보여준다. 특히 정서인식과 정서조절 전략을 향상시키는 코칭 개입, 그리고 그 릿을 강화하는 맞춤형 코칭 프로그램이 구성원의 피드백 수용성 제고에 효과적 일 수 있음을 시사한다.

본 연구는 Deepseek와 ChatGPT가 중국 대학 한국어 학습자들의 쓰 기 텍스트에 대해 제공하는 피드백 양상을 살피고, AI 피드백의 활용 방 안을 모색하는 데 목적이 있다. 이를 위해 한국어 학습자 30명을 대상으 로 쓰기 텍스트를 수집하고, 두 도구가 텍스트에 대해 제공한 피드백을 분석하였다. 분석 결과, 두 도구 모두 평가 기준에 기반하여 피드백을 제 공하고, 학습자가 수용하기 쉽도록 문제점과 개선 방향을 명확하게 제시 하였다. 대부분의 피드백은 정확도가 높은 편이었으나, 평가 범주와 오류 유형이 부합하지 않는 경우도 일부 나타나고 있었고, 특히 Deepseek의 경우 쓰기 주제를 잘못 파악하여 내용적 측면에서 부적절한 피드백을 제 공한 사례가 많았다. 두 도구 모두 쓰기 텍스트의 내용에 따라 피드백을 제공하여 우선순위는 두드러지지 않았고, Deepseek가 ChatGPT에 비해 지지적인 어조를 더 많이 사용하는 것으로 나타났다. 본 연구는 생성형 AI를 활용한 한국어 교육 및 연구를 위해 기초적인 자료로 사용할 수 있 을 것이다.

Background: Flexible flatfoot impairs gait and posture by weakening arch support, potentially leading to musculoskeletal dysfunction. Strengthening exercises, such as the short foot exercise (SFE), have shown promise in correcting this condition. Objectives: This study aimed to investigate the effects of SFE with visual feedback on medial arch height and foot function in adults with flexible flatfoot. Design: Experimental research. Methods: Adults diagnosed with flexible flatfoot were randomly assigned to either an experimental or control group. The experimental group performed SFE with visual feedback, whereas the control group performed the same exercises without feedback. Both groups trained three times per week for five weeks. Outcome measures included the Navicular Drop Test (NDT), YBalance Test (YBT), and Tetrax postural analysis. Results: In the NDT, both groups showed significant improvements (P<.05), while in the YBT, only the experimental group showed a significant improvement (P<.05). In contrast, there were no significant changes in the Weight Distribution Index (WDI) and Stability Test (ST) areas of the Tetrax system in either group (P>.05). Conclusion: SFE effectively improved arch height regardless of visual feedback, though only the visual feedback group showed significant improvements in dynamic balance. However, between-group differences were not statistically significant, suggesting that visual feedback provides subtle rather than substantial additional benefits. Further research with larger samples is needed to establish the clinical value of adding visual feedback to SFE protocols.

The purpose of this paper is to investigate the effects of students’ peer feedback in collaborative English writing of general English classes. For this reason, online pre-and post-surveys were conducted on 33 students enrolled in D University in Chuncheongnam-do. The study results showed that in terms of the affective aspects of peer feedback, the burden of providing feedback, anxiety about English proficiency and negative feedback, and discomfort with negative feedback decreased. At the same time, the perceived usefulness of peer feedback increased. However, cooperation and responsibility, trust in peer feedback, motivation, and the burden of interaction did not show statistically significant differences. In addition, students mentioned that they benefited from peer feedback in the order of grammar, text structure, and vocabulary. As positive aspects, they highlighted objectivity, assistance in English learning, and increased confidence. On the other hand, they pointed out their discomfort in giving feedback due to their low English proficiency and the possibility of providing incorrect information. Based on the results, several implications are discussed.

이 논문은 S대학교 ‘사고와 글쓰기’ 교과에서 AI 자동 피드백 프로그 램 키위(KEEwi)를 활용한 수업 사례를 분석하고 그것이 지닌 효과와 한 계에 관해 탐색할 것을 목적으로 한다. 먼저 2장에서는 AI 자동 피드백 프로그램의 동향과 원리를 살펴보고 3장에서는 키위 프로그램을 활용한 수업 운영과 구체적 사례에 관해 설명하였다. 4장에서는 설문조사 분석 을 통한 학습자들의 프로그램 만족도와 교육 효과 및 한계에 관해 논의 하였다. 그 결과 이 프로그램은 즉각적이고 반복적인 피드백을 통해 학 습자의 학습 능률을 향상시킬 뿐 아니라 수업에 대한 흥미를 유발하여 글쓰기 역량을 강화하는 데 긍정적인 영향을 끼치는 것으로 드러났다. 또한 교수자에게는 글쓰기 교육의 보조 도구이자 협력자로 기능하며 학 습자 중심의 피드백 환경을 조성하는 데 기여하는 것으로 파악된다. 그 러나 키위 엔진은 완전한 대체 도구가 아니며 자체적인 오류와 문제를 가지고 있어 절대적인 평가 지표로 활용하는 것에는 한계가 있음이 확인 되었다. 이러한 연구는 대학 글쓰기 교양수업에서 AI 자동 피드백 프로 그램이 학습자와 교수자에게 끼치는 영향과 그 한계를 파악하는 데 도움 이 되리라 생각한다.