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Journal articles on the topic 'AI-generated feedback'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Tran, Thi Thanh Thao. "Enhancing EFL Writing Revision Practices: The Impact of AI- and Teacher-Generated Feedback and Their Sequences." Education Sciences 15, no. 2 (2025): 232. https://doi.org/10.3390/educsci15020232.

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The use of different generative AIs such as OpenAI’s ChatGPT, Microsoft Copilot, or Google’s Gemini has been implemented and studied in different aspects of language education. However, exploring how the combination of teacher-generated feedback and AI-generated feedback influences student revision practices in EFL academic writing remains largely unexplored. To fill in the gap, this preliminary study investigates the impact of two forms of feedback, including teacher-generated feedback and AI-generated feedback, as well as the orders in which the two types of feedback have been executed, that

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Azizah, Rizki, Agus Wijayanto, and Malikatul Laila. "Can AI-Generated Feedback Overshadow Teachers’ Existence?" Dinasti International Journal of Education Management And Social Science 6, no. 5 (2025): 3505–21. https://doi.org/10.38035/dijemss.v6i5.4503.

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AI-generated feedback is expected to be exploited for enhancing learners’ speaking skills and maintaining learners’ emotional state. The magical power might overshadow teachers’ feedback, as well. For further investigating learners’ perception of AI-generated feedback compared to teachers’ feedback in fostering learners’ speaking skills and maintaining emotional state, this study employed a mix-method. Data were collected through a 15-item questionnaire and semi-structured interviews with Grade XI students at Budi Utomo Senior High School. The questionnaire examined the intensity of feedback r

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Tang, Xiaoyi. "An empirical study on the effects of AI-generated feedback on English writing." 国际化教育科学与理论 3, no. 9 (2024): 18. https://doi.org/10.26789/ijest.v3i9.2010.

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The study aims to evaluate the effectiveness of AI-generated writing feedback and investigate students' preferences between AI and teacher feedback. The research involved 270 college first-year students in an eight-week quasi-experimental design and weekly surveys. The results indicate that AI-generated feedback did not result in superior linguistic progress among students compared to those who received teacher feedback. Findings also reveal a near even split in preference for AI-generated or teacher feedback, with clear advantages to both feedback forms. The study implies that AI-generated fe

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Abdelouahd, Bouzar1* Khaoula El Idrissi2 Tayeb Ghourdou3 Nirhech Ali4. "Supervisory Feedback vs. AI: A Comparative Study on Postgraduate Student Satisfaction." ISRG Journal of Arts Humanities & Social Sciences (ISRGJAHSS) III, no. I (2025): 465–74. https://doi.org/10.5281/zenodo.14862530.

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<strong>Abstract</strong> <em>Feedback plays a crucial role in postgraduate research supervision, influencing students’ academic progress and satisfaction. Traditional supervisory feedback is valued for its engagement and contextual relevance, while artificial intelligence (AI)-generated feedback, particularly from models like ChatGPT, is gaining attention for its clarity and accessibility. However, limited quantitative research has explored students’ comparative perceptions of AI versus human feedback. This study examines how postgraduate students perceive ChatGPT-generated feedba

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Sazandeh, Mozhdeh, and Maryam Beiki. "Comparing AI and Teacher Corrective Feedback on Iranian EFL Learners’ Essay Writing Skills." Asian Journal of Education and Social Studies 51, no. 5 (2025): 473–86. https://doi.org/10.9734/ajess/2025/v51i51934.

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Aims: This study aimed to compare the effectiveness of teacher-generated versus AI-generated corrective feedback on the essay writing skills of Iranian intermediate EFL learners. Study Design: Quasi-experimental design. Place and Duration of Study: Department of TEFL, Islamic Azad University, North Tehran Branch, conducted over a 16-week period in 2025. Methodology: A total of 80 Iranian intermediate EFL learners were selected through convenience sampling and divided into two equal groups: Teacher-Generated Corrective Feedback Group (TGFG, n=40) and AI-Generated Corrective Feedback Group (AGFG

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Prof. (Dr) Satya Singh and Ratnesh Kumar Sharma. "Reinforcement Learning from AI Feedback A Review." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 4 (2024): 306–11. http://dx.doi.org/10.32628/cseit24104135.

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Reinforcement Learning from AI Feedback (RLAIF) is a big step forward compared to Reinforcement Learning from Human Feedback (RLHF). It's especially useful for large language models like GPT-4. RLAIF is better because it can handle more data at scale and is more efficient. It uses AI-generated feedback instead of human feedback. This shift to AI-generated feedback enhances the efficiency and speed of training AI systems. Additionally, RLAIF optimizes the AI's ability to align with desired outcomes, although it may not directly improve understanding human preferences. RLAIF uses a Preference Mo

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Wang, Dan. "Teacher- Versus AI-Generated (Poe Application) Corrective Feedback and Language Learners’ Writing Anxiety, Complexity, Fluency, and Accuracy." International Review of Research in Open and Distributed Learning 25, no. 3 (2024): 37–56. http://dx.doi.org/10.19173/irrodl.v25i3.7646.

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This study examines the effects of corrective feedback (CF) on language learners’ writing anxiety, writing complexity, fluency, and accuracy, and compares the effectiveness of feedback from human teachers with an AI-driven application called Poe. The study included three intact classes, each with 25 language learners. Using a quasi-experimental design with pretest and posttest measures, one class received feedback from the teacher, one from the Poe application, and the third received no response to their writing. Data were generated though tests and a writing anxiety scale developed for the st

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Chan, Sumie, Noble Lo, and Alan Wong. "Generative AI and Essay Writing: Impacts of Automated Feedback on Revision Performance and Engagement." rEFLections 31, no. 3 (2024): 1249–84. https://doi.org/10.61508/refl.v31i3.277514.

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This study investigates the impact of feedback generated by large language models (LLMs) on improving the essay-writing skills of first-year university students in Hong Kong. Specifically, it examines how generative AI supports students in revising their essays, enhances engagement with writing tasks, and influences their emotional responses during the revision process. The study followed a randomized controlled trial design, with one group of students receiving AI-generated feedback on their essay drafts while a control group did not. A mixed-methods approach was used to evaluate the feedback

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Burner, Tony, Yngve Lindvig, and Jarl Inge Wærness. "“We Should Not Be Like a Dinosaur”—Using AI Technologies to Provide Formative Feedback to Students." Education Sciences 15, no. 1 (2025): 58. https://doi.org/10.3390/educsci15010058.

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Artificial intelligence (AI) technologies have the potential to enhance learning, teaching, and assessment by providing AI-generated feedback to students. This study used five different AI plug-ins and four different knowledge bases for the optimization of feedback in classroom formative assessments. The sample consisted of 26 students and 13 teachers at five secondary schools in Norway. Interviews and unstructured observations were used to collect data. The findings indicate that AI-generated feedback is appreciated by both students and teachers. It provides immediate subject-specific, concre

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Amoush, Kholoud H., and Amal A. Alhosban. "Mastering EFL Writing With ChatGPT: A Systematic Review of Benefits, Challenges, and Best Practices." Theory and Practice in Language Studies 15, no. 8 (2025): 2576–83. https://doi.org/10.17507/tpls.1508.14.

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The rapid advancement of artificial intelligence (AI) in English as a Foreign Language (EFL) instruction—especially in writing—is unprecedented. Among AI tools, ChatGPT stands out as a powerful resource, offering real-time, contextual feedback that goes beyond simple grammar corrections. This systematic review draws on insights from 21 studies to evaluate the impact of ChatGPT in improving EFL students’ writing skills. It looks at how AI-generated feedback compares to human feedback and reflects on the broader effect of using ChatGPT in EFL writing instruction. Research findings showed that Ch

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Joanne, Nabwire Lyanda, and Oliech Owidi Salmon. "Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning." International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) 12, no. 2 (2025): 1–10. https://doi.org/10.5281/zenodo.15130275.

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<strong>Abstract:</strong><em> </em>The integration of Artificial Intelligence (AI) in teaching has transformed conventional teacher training methods, offering AI-driven feedback systems, interactive simulations, and adaptive learning environments. This study explored the role of AI, particularly generative models like ChatGPT, in enhancing lesson delivery, instructional feedback, and teacher engagement in micro- teaching. AI-powered platforms provide real-time, systematic, and personalized feedback, analyzing verbal communication, lesson structuring, and classroom engagement techniques to imp

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Chan, Sumie Tsz Sum, Noble Po Kan Lo, and Alan Man Him Wong. "Enhancing university level English proficiency with generative AI: Empirical insights into automated feedback and learning outcomes." Contemporary Educational Technology 16, no. 4 (2024): ep541. http://dx.doi.org/10.30935/cedtech/15607.

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This paper investigates the effects of large language model (LLM) based feedback on the essay writing proficiency of university students in Hong Kong. It focuses on exploring the potential improvements that generative artificial intelligence (AI) can bring to student essay revisions, its effect on student engagement with writing tasks, and the emotions students experience while undergoing the process of revising written work. Utilizing a randomized controlled trial, it draws comparisons between the experiences and performance of 918 language students at a Hong Kong university, some of whom rec

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Dazzeo, Robin. "AI-Enhanced Writing Self- Assessment." Journal of Technology-Integrated Lessons and Teaching 3, no. 2 (2024): 80–85. https://doi.org/10.13001/jtilt.v3i2.9119.

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This technology-rich, three-day lesson for 9th-12th grade English Language Arts students leverages artificial intelligence (AI)-enhanced rubrics and writing analysis tools (Ouyang & Jiao, 2021) to improve student writing and self-assessment skills. Students explore AI-enhanced digital rubrics, use AI tools to analyze their writing, and apply AI-generated feedback to revise their work. This approach enhances current writing assignments and develops critical thinking skills and digital literacy. Assessments include AI-generated feedback reports, peer evaluations, and final revised writing sa

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Yang, Hongli, Yu Zhang, and Jixuan Guo. "Exploring the Effectiveness of Cooperative Pre-Service Teacher and Generative AI Writing Feedback on Chinese Writing." Behavioral Sciences 15, no. 4 (2025): 518. https://doi.org/10.3390/bs15040518.

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Due to their efficiency, stability, and enhanced language comprehension and analysis capabilities, generative AIs have attracted increasing attention in the field of writing as higher-level automated writing evaluation (AWE) feedback tools. However, few studies have examined the impact of pre-service teachers using generative AI in combination with their own teaching experience to provide feedback on Chinese writing. To fill this gap, based on 1035 writing feedback texts, we examined the differences in writing feedback between 11 pre-service teachers and Erine Bot (a generative AI) and intervi

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Solak, Ekrem. "Examining Writing Feedback Dynamics from ChatGPT AI and Human Educators: a Comparative Study." Pedagogika-Pedagogy 96, no. 7 (2024): 955–69. http://dx.doi.org/10.53656/ped2024-7.05.

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As technology increasingly influences educational practices, it is crucial to comprehend the impact and effectiveness of AI in comparison to human-driven methodologies. In the field of language learning and writing instruction, one notable development is the emergence of AI-generated writing feedback tools. These tools, exemplified by ChatGPT in this study, promise efficiency, objectivity, and detailed insights for learners. This study aims to investigate the comparative effectiveness of an artificial intelligence (AI) automated writing feedback tool and human teacher feedback in enhancing the

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Sadigzade, Zarifa. "AI-Powered Feedback in ESL Writing Classes: Pedagogical Opportunities and Ethical Concerns." Journal of Azerbaijan Language and Education Studies 2, no. 4 (2025): 5–17. https://doi.org/10.69760/jales.2025004000.

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Background: Feedback is crucial in second-language (L2) writing instruction for guiding student revisions and improvement. With the rise of artificial intelligence (AI), tools like grammar checkers and chatbots are increasingly providing automated feedback in ESL (English as a Second Language) writing contexts. Purpose: This study explores the pedagogical benefits and ethical challenges of integrating AI-generated feedback in ESL writing classes. It asks how AI feedback can enhance learning and what concerns arise regarding its use. Method: A thematic literature review was conducted, framing t

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Perdana, Indra, Joni Bungai, and Ilham Ilham. "The Students' Perceptions of AI-Generated and Teacher Feedback in English Writing Skills: A Comparative Study." Jurnal Pendidikan Bahasa Inggris undiksha 13, no. 1 (2025): 22–31. https://doi.org/10.23887/jpbi.v13i1.92668.

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Both AI and teacher feedback have benefits in improving students' writing skills. However, there are still differences in perception about the effectiveness of each type of feedback in helping students develop their writing abilities. This study aims to compare students' perceptions of the effectiveness of feedback provided by AI and teachers in enhancing writing skills. A descriptive-quantitative research method was used to conduct a survey among 62 eleventh-grade students in Palangka Raya. A Likert scale assessment was used in the questionnaire to evaluate ten indicators, including unity, co

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Yu, Hetian, and Qin Xie. "Generative AI vs. Teachers: Feedback Quality, Feedback Uptake, and Revision." Language Teaching Research Quarterly 47 (April 2025): 113–37. https://doi.org/10.32038/ltrq.2025.47.07.

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Generative AI has stormed the education community worldwide. Gen-AI’s potential to assist teachers in providing feedback to students is the focus of the current research. Specifically, the research investigated how similar or different AI-generated feedback was from teacher feedback, and whether there were differences in students’ responses to the two feedback sources. The research adopted a 2x2 counter-balance experimental design (Latin Square), whereby the participants were divided into two groups to write on two topics, receive feedback, and resubmit the revised work. The participants were

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Kunz, Sibylle, and Adrienne Steffen. "Criticize my Code, not me: Using AI-generated Feedback in Computer Science Teaching." International Conference on Gender Research 7, no. 1 (2024): 200–207. http://dx.doi.org/10.34190/icgr.7.1.2002.

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Large Language Models (LLMs) like ChatGPT can help teachers to tailor learning tasks for their students, combining learning objectives and storytelling to raise interest in the subject. AI-based learning task design can help to support competency-based learning, especially for girls in STEM courses like computer science, where otherwise the “Leaky STEM pipeline” (Speer 2023) leads to a constant loss of female students over school time. LLMs support many steps of the creation cycle of learning tasks. One important step is the feedback process between teachers and students during and after solvi

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Hanifah, Laura, and Nyak Mutia Ismail. "HUMAN-CENTERED AI IN LANGUAGE CLASSROOMS." Proceedings of International Conference on Education 3, no. 1 (2025): 239–46. https://doi.org/10.32672/pice.v3i1.3481.

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This qualitative case study investigated how teachers and students at senior high schools in Banda Aceh experience the integration of artificial intelligence (AI) tools in classroom-based language learning. Adopting a human-centered perspective, the study addressed two primary questions: (1) how do teachers navigate their roles when integrating AI into classroom instruction, and (2) what are students’ experiences of receiving AI-generated feedback in a teacher-guided learning environment? Data were collected through semi-structured interviews with English teachers, Focused-Group Discussions (F

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Ozdere, Mustafa. "AI in Academic Writing: Assessing the Effectiveness, Grading Consistency, and Student Perspectives of ChatGPT and You.com for EFL Students." International Journal of Technology in Education 8, no. 1 (2025): 123–54. https://doi.org/10.46328/ijte.1001.

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This study evaluates the impact of AI-generated feedback and targeted training sessions on the writing skills of 16 B1-B2 level students majoring in English Language and Literature (ELL) or English Language Teaching (ELT) at Niğde Ömer Halisdemir University, School of Foreign Languages, during the 2023-2024 spring term. Using a mixed-methods approach, the research assessed students' writing across four paragraph types—opinion, cause/effect, reason/result, and advantage/disadvantage—employing a rubric that rates Grammar and Mechanics, Sentence Structure, Cohesion and Coherence, Argumentation/Co

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Bulut, Okan, and Tarid Wongvorachan. "Feedback Generation through Artificial Intelligence." Open/Technology in Education, Society, and Scholarship Association Conference 2, no. 1 (2022): 1–9. http://dx.doi.org/10.18357/otessac.2022.2.1.125.

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Feedback is an essential part of the educational assessment that improves student learning. As education changes with the advancement of technology, educational assessment has also adapted to the advent of Artificial Intelligence (AI). Despite the increasing use of online assessments during the last decade, a limited number of studies have discussed the feedback generation process as implemented through AI. To address this gap, we propose a conceptual paper to organize and discuss the application of AI in the feedback generation and delivery processes. Among different branches of AI, Natural L

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Elmotri, Bilel, Radhia Harizi, Amel Boujlida, et al. "The Impact of AI-Generated Feedback Explicitness (Generic vs. Specific) on EFL Students’ Use of Automated Written Corrective Feedback." Arab World English Journal 16, no. 1 (2025): 384–402. https://doi.org/10.24093/awej/vol16no1.24.

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This study investigates how AI-driven Automated Written Corrective Feedback can facilitate EFL writing, teaching, and evaluation in Arab schools and universities. The study addresses a significant research gap in the Computer-Assisted Language Learning literature by examining the impact of Automated Essay Scoring systems’ feedback on EFL University students’ writing. AI-powered AWCF tools have the potential to enhance writing accuracy, provide individualized feedback, and reduce teachers’ workload by offering automated yet effective corrective feedback. By analyzing EFL learners’ and instructo

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Naseer, Fawad, and Sarwar Khawaja. "Mitigating Conceptual Learning Gaps in Mixed-Ability Classrooms: A Learning Analytics-Based Evaluation of AI-Driven Adaptive Feedback for Struggling Learners." Applied Sciences 15, no. 8 (2025): 4473. https://doi.org/10.3390/app15084473.

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Adaptation through Artificial Intelligence (AI) creates individual-centered feedback strategies to reduce academic achievement disparities among students. The study evaluates the effectiveness of AI-driven adaptive feedback in mitigating these gaps by providing personalized learning support to struggling learners. A learning analytics-based evaluation was conducted on 700 undergraduate students enrolled in STEM-related courses across three different departments at Beaconhouse International College (BIC). The study employed a quasi-experimental design, where 350 students received AI-driven adap

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Naz, Irum, and Rodney Robertson. "Exploring the Feasibility and Efficacy of ChatGPT3 for Personalized Feedback in Teaching." Electronic Journal of e-Learning 22, no. 2 (2024): 98–111. http://dx.doi.org/10.34190/ejel.22.2.3345.

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This study explores the feasibility of using AI technology, specifically ChatGPT-3, to provide reliable, meaningful, and personalized feedback. Specifically, the study explores the benefits and limitations of using AI-based feedback in language learning; the pedagogical frameworks that underpin the effective use of AI-based feedback; the reliability of ChatGPT-3’s feedback; and the potential implications of AI integration in language instruction. A review of existing literature identifies key themes and findings related to AI-based teaching practices. The study found that social cognitive theo

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Zhang, Shuting. "Chatgpt Assisted Teachers in Improving Formative Assessment." Journal of Education, Humanities and Social Sciences 40 (November 7, 2024): 27–32. http://dx.doi.org/10.54097/qz3kbj17.

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In recent years, the application of artificial intelligence technology in education has received widespread attention, but its specific utility and challenges in formative assessment remain underexplored. This research paper investigates the value of ChatGPT in aiding teachers with exam design and providing student feedback. Through comprehensive interviews, the study analyses the actual effectiveness and challenges of using ChatGPT to enhance teaching efficiency and optimize student feedback. The findings indicate that ChatGPT offers significant advantages in generating diverse assessment que

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Ortiz, Marie Grace, and Menchita Dumlao. "AI-Driven Insights from Student Feedback for Teacher Improvement." Journal of Interdisciplinary Perspectives 3, no. 8 (2025): 503–13. https://doi.org/10.69569/jip.2025.418.

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This study used Natural Language Processing (NLP) and Artificial Intelligence (AI) to analyze ten years of teacher evaluations. The research leveraged VADER and NRC for sentiment analysis and LDA for topic modeling to extract key themes. The Google Gemini AI model then generated actionable recommendations for pedagogical improvement. Analysis of 9,052 textual comments revealed a predominantly positive (71%) to neutral (27%) comments, and LDA identified eight distinct topics. The AI-driven analysis successfully provided targeted suggestions for pedagogical enhancement, offering a pathway toward

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Ms. G. Illakiya, G.V. Jeeshitha, R. Manjushree, and P. Harshini. "AI Based Hallucination Detector." International Research Journal on Advanced Science Hub 6, no. 12 (2024): 381–89. https://doi.org/10.47392/irjash.2024.050.

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The main goal of this project is to create an advanced system that can accept questions submitted by users, produce AI-generated answers, and guarantee their accuracy by using an integrated validation method. This involves connecting to external web APIs that have access to trustworthy and authoritative sources, allowing the system to compare AI-generated responses with verified factual data in real time. If the AI-generated answer is accurate, the system will show a confirmation, providing users with assurance of its reliability. However, if the answer is incorrect, the system will flag the e

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Azaiz, Imen, Oliver Deckarm, and Sven Strickroth. "AI-Enhanced Auto-Correction of Programming Exercises: How Effective is GPT-3.5?" International Journal of Engineering Pedagogy (iJEP) 13, no. 8 (2023): 67–83. http://dx.doi.org/10.3991/ijep.v13i8.45621.

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Timely formative feedback is considered as one of the most important drivers for effective learning. Delivering timely and individualized feedback is particularly challenging in large classes in higher education. Recently Large Language Models such as GPT-3 became available to the public that showed promising results on various tasks such as code generation and code explanation. This paper investigates the potential of AI in providing personalized code correction and generating feedback. Based on existing student submissions of two different real-world assignments, the correctness of the AI-ai

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L, Godlin Atlas, Mageshkumar C, Shiny K.V., Bhavana D, and Madhumitha V. "GENERATIVE AI IN CREATIVE INDUSTRIES USING GANS FOR MUSIC AND ART GENERATION WITH HUMAN-AI CO-CREATION." ICTACT Journal on Soft Computing 15, no. 3 (2025): 3653–61. https://doi.org/10.21917/ijsc.2025.0507.

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The rapid development of Generative AI, particularly Generative Adversarial Networks (GANs), has revolutionized the creative industries, including music and art generation. Artists and musicians are increasingly integrating AI to co-create novel compositions and artworks, expanding creative boundaries and fostering innovative forms of artistic expression. Despite the promising potential, the adoption of AI in these fields raises concerns regarding creativity, authorship, and the preservation of artistic authenticity. This study explores the application of GANs for music and art generation, foc

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Cioffi, Valeria, Ottavio Ragozzino, Lucia Luciana Mosca, et al. "Can AI Technologies Support Clinical Supervision? Assessing the Potential of ChatGPT." Informatics 12, no. 1 (2025): 29. https://doi.org/10.3390/informatics12010029.

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Clinical supervision is essential for trainees, preventing burnout and ensuring the effectiveness of their interventions. AI technologies offer increasing possibilities for developing clinical practices, with supervision being particularly suited for automation. The aim of this study is to evaluate the feasibility of using ChatGPT-4 as a supervisory tool in psychotherapy training. To achieve this, a clinical case was presented to three distinct groups (untrained AI, pre-trained AI, and qualified human supervisor), and their feedback was evaluated by Gestalt psychotherapy trainees using a Liker

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Noerjaman, Lutfi Arief, Ruswan Dallyono, and Farida Hidayati. "Enhancing job application writing: A comparative case study of AI and human feedback on grammar and mechanics." Journal of English Language Teaching Innovations and Materials (Jeltim) 7, no. 1 (2025): 54–72. https://doi.org/10.26418/jeltim.v7i1.89240.

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Submission of well-structured job application letters is an essential aspect of business and government recruitment processes. However, due to difficulties with grammar and mechanics, many students, fresh graduates included, fail to produce high-quality job applications. While human reviewers typically give feedback to enhance these skills, Artificial Intelligence (AI) has brought about automated feedback systems. The research compared the performance of two artificial intelligence models, Gemini and ChatGPT, and human experts in assessing job application letters. The study compared feedback i

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Huang, Xiaojing, Xu Han, and Anqi Dou. "Generative AI in EFL Speaking Instruction: Teachers’ Reflections on Effectiveness and Implementation Barriers." Journal of Higher Vocational Education 1, no. 5 (2024): 13–18. https://doi.org/10.62517/jhve.202416503.

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This study explores the use of generative AI tools in enhancing English as a Foreign Language (EFL) speaking skills, focusing on the experiences of 15 college English teachers from various universities in Hainan, China. Using semi-structured interviews and focus group discussions, the research identifies both benefits and challenges in integrating AI-driven applications for speaking instruction. The findings indicate that generative AI tools, such as AI agents, provide immediate personalized feedback, which enhances student engagement and allows for extensive speaking practice. However, the st

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An, Jingkun, Yinghao Zhu, Zongjian Li, et al. "AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 2 (2025): 1746–54. https://doi.org/10.1609/aaai.v39i2.32168.

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Text-to-Image (T2I) diffusion models have achieved remarkable success in image generation. Despite their progress, challenges remain in both prompt-following ability, image quality and lack of high-quality datasets, which are essential for refining these models. As acquiring labeled data is costly, we introduce AGFSync, a framework that enhances T2I diffusion models through Direct Preference Optimization (DPO) in a fully AI-driven approach. AGFSync utilizes Vision-Language Models (VLM) to assess image quality across style, coherence, and aesthetics, generating feedback data within an AI-driven

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Anđić, Branko. "ARTIFICIAL INTELLIGENCE AND DEEPFAKE LEARNING IN HIGHER EDUCATION." Journal of Baltic Science Education 24, no. 1 (2025): 4–6. https://doi.org/10.33225/jbse/25.24.04.

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Higher education appears to be undergoing the most significant transformations due to the integration of artificial intelligence into teaching, learning, feedback provision, assessment, writing, and the growing role of AI in student research. Several aspects of AI in education have already been studied, such as institutional adoption policies and guidelines (Jin et al., 2025), Intelligent Tutoring Systems, and Automated Assessment and Feedback (Ramadhan et al., 2024). On the other hand, certain topics, such as deepfakes and their consequences for learning, remain underexplored. Deepfakes refer

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Iatrellis, Omiros, Areti Bania, Nicholas Samaras, Ioanna Kosmopoulou, and Theodor Panagiotakopoulos. "ChatGPT in Doctoral Supervision: Proposing a Tripartite Mentoring Model for AI-Assisted Academic Guidance." International Journal of Doctoral Studies 20 (2025): 009. https://doi.org/10.28945/5579.

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Aim/Purpose: The potential of Generative AI in education is expanding, yet its role in PhD mentoring and academic guidance remains underexplored. This study evaluates how ChatGPT-generated recommendations can support PhD research, particularly in fostering sustainable and resource-efficient doctoral education. Background: This study examines the ability of ChatGPT to provide structured guidance and actionable insights in PhD supervision. Using a real-world case study on disaster risk management, the research evaluates AI-generated recommendations across different prompt structures to determine

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Kanchana, S., and Prativa Rani Saha. "Evolving Student Assessment: AI-Driven Rubrics for Personalized and Equitable English Language Learning." Journal of Engineering Education Transformations 38, IS2 (2025): 584–90. https://doi.org/10.16920/jeet/2025/v38is2/25072.

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Technological advancements have been reshaping the paradigms of educational scenarios through tools and platforms that enhance the academic experience. The demands of Industry 4.0 and the implications of Moore's Law accelerated these changes paving the way for imbibing innovative approaches in education. This study delves into developing and applying AI-driven rubrics to foster personalized and equitable assessments in English language learning for engineering undergraduates. By leveraging AI tools, the tailored rubrics provide personalized feedback, minimize subjectivity, and improve grading

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Firda Ziyana Untsa and Ulin Nuha. "ENHANCING OR INHIBITING? THE DUAL ROLE OF AL TOOLS IN EFL WRITING PRACTICES." Esteem Journal of English Education Study Programme 8, no. 2 (2025): 89–102. https://doi.org/10.31851/esteem.v8i2.18553.

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This study investigates the dual role of Artificial Intelligence (AI) tools in supporting English as a Foreign Language (EFL) writing. Tools like Grammarly and ChatGPT are found to enhance students’ technical writing accuracy such as grammar, vocabulary, and sentence structure by offering instant feedback. However, concerns persist regarding their limited contribution to the development of higher-order writing skills. Conducted with 100 Indonesian tertiary-level EFL students, this mixed-methods research used surveys and interviews to evaluate the impact of AI tools on writing performance. Quan

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Nnenna, Ijeoma Okeke, Anne Alabi Olufunke, Ngochindo Igwe Abbey, Chrisanctus Ofodile Onyeka, and Paul-Mikki Ewim Chikezie. "AI in customer feedback integration: A data-driven framework for enhancing business strategy." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2036–52. https://doi.org/10.5281/zenodo.15051426.

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The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources,

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Zhang, Zhengdong, Zihan Dong, Yang Shi, Thomas Price, Noboru Matsuda, and Dongkuan Xu. "Students’ Perceptions and Preferences of Generative Artificial Intelligence Feedback for Programming." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23250–58. http://dx.doi.org/10.1609/aaai.v38i21.30372.

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The rapid evolution of artificial intelligence (AI), specifically large language models (LLMs), has opened opportunities for various educational applications. This paper explored the feasibility of utilizing ChatGPT, one of the most popular LLMs, for automating feedback for Java programming assignments in an introductory computer science (CS1) class. Specifically, this study focused on three questions: 1) To what extent do students view LLM-generated feedback as formative? 2) How do students see the comparative affordances of feedback prompts that include their code, vs. those that exclude it?

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Jacobsen, Lucas Jasper, and Kira Elena Weber. "The Promises and Pitfalls of Large Language Models as Feedback Providers: A Study of Prompt Engineering and the Quality of AI-Driven Feedback." AI 6, no. 2 (2025): 35. https://doi.org/10.3390/ai6020035.

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Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning outcomes. However, few empirical studies have directly compared the quality of LLM feedback with feedback from novices and experts. This study investigates (1) the types of prompts needed to ensure high-quality LLM feedback in teacher education and (2) how feedback from novices, experts, and LLMs compares in terms of quality. Methods: To address these

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Muhammad Umar Habib, Wasim Akram, Dr. Sadaf Saleem, and Andleeb Shakoor. "How Cognitive Dissonance Affects Student Engagement and Learning in AI Powered Education Systems." Critical Review of Social Sciences Studies 3, no. 1 (2025): 1905–17. https://doi.org/10.59075/r1zta509.

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This paper examines the impact of cognitive dissonance on student engagement and learning adaptation in AI-powered education. Based on a quantitative research design, data from 270 university teachers in Punjab were retrieved using a self-administered questionnaire. Findings show a moderate negative correlation between cognitive dissonance and student engagement with r = -0.542, p = 0.001, indicating that increased dissonance results in lower motivation and participation levels. A significant effect of the regression analysis for AI-generated feedback is that motivation and learning adaptation

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Santiago, Jose Maria, Moritz Sendner, David Ralser, and Alexander Meschtscherjakov. "The AI of Oz: A Conceptual Framework for Democratizing Generative AI in Live-Prototyping User Studies." Applied Sciences 15, no. 10 (2025): 5506. https://doi.org/10.3390/app15105506.

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Commonly used methods in User-Centered Design (UCD) can face challenges in incorporating user feedback during early design stages, often resulting in extended iteration cycles. To address this, we explore the following question: “How can generative artificial intelligence (AI) be utilized to enable prototyping within user studies to facilitate immediate user feedback integration and validation?” We introduce a conceptual framework for live-prototyping, where designers modify AI-generated components of a prototype in real time through a separate control interface during user testing. This appro

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Liu, Jinmei, Hua Yuan, Xue Lin, and Nianqiang Li. "Teaching Reform of “Telecommunication Electronic Circuits” Based on the Integration of Artificial Intelligence and Human Wisdom." International Journal of Education, Culture and Society 10, no. 2 (2025): 120–25. https://doi.org/10.11648/j.ijecs.20251002.17.

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As an important compulsory course for the students who major in telecommunications engineering, the Telecommunication Electronic Circuits (High-Frequency Electronic Circuits) course confronts two major challenges: high knowledge complexity and limited resources for practical engineering training. In order to overcome the challenges and promote teaching effects, we utilize the scheme of integrating artificial intelligence (AI) technology with human wisdom in teaching the Telecommunication Electronic Circuits course. The scheme combines the advantages of both AI technology and human wisdom. AI t

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Nnenna Ijeoma Okeke, Olufunke Anne Alabi, Abbey Ngochindo Igwe, Onyeka Chrisanctus Ofodile, and Chikezie Paul-Mikki Ewim. "AI in customer feedback integration: A data-driven framework for enhancing business strategy." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 2036–52. http://dx.doi.org/10.30574/wjarr.2024.24.1.3207.

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Abstract:

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources,

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R. Brawn, James. "In Search of the Prompt that Produces useful Written Corrective Feedback for L2 Composition Classes." International Journal of Education (IJE) 12, no. 4 (2024): 17–24. https://doi.org/10.5121/ije2024.12402.

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The use of artificial intelligence (AI) in language education may be in its infancy, but technological advances, especially natural language processing, will lead to its widespread adoption far sooner than many may think. For example, large language models (LLMs) like ChatGPT are often used when individuals utilize AI systems. This means that researchers in second language learning must begin evaluating the utility of AI-based tools for second language instruction. This study describes the importance of prompt engineering in designing effective prompts for second-language writing feedback. Thi

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Mutahira Bukhari, Dr. Mudasar Jahan, and Mahnoor Ghani Sheikh. "Transforming ESL Teaching: Exploring the Potential of AI-Powered Tools in Supporting Teachers in the Classroom." Physical Education, Health and Social Sciences 3, no. 1 (2025): 60–73. https://doi.org/10.63163/jpehss.v3i1.127.

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This qualitative study explores the experiences and perceptions of English as a Second Language (ESL) instructors in higher education institutions in Pakistan regarding the use of Artificial Intelligence (AI)-powered tools in language instruction. Aiming to address the research gap in the literature on AI integration in ESL instruction in Pakistan, this study examines the current integration of AI, identifies potential applications to enhance instructor performance and student learning outcomes, and explores the challenges and limitations associated with AI integration. Through semi-structured

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Dr. G. Sivakumar, Sangeetha.V, Khushbu Kumari, and Rashika C. "Automated Group Discussion Feedback and Scoring with AI." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1117–25. https://doi.org/10.47392/irjaeh.2025.0161.

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In traditional group discussions, the absence of a human moderator can lead to issues such as dominance by certain individuals, lack of adherence to discussion rules, and insufficient feedback for participants. To address these challenges, an AI-driven group discussion platform is being developed to ensure structured, fair, and productive interactions. The platform will monitor discussions via video, using machine learning to analyze participants' behavior and adherence to ethical guidelines. AI will provide real-time guidance and personalized feedback, assessing individual contributions to hi

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Bao, Ran, and Liang Sile. "Empowering English Vocabulary Instruction through Generative Artificial Intelligence: The AEFC Method." Journal of Educational Research Progress 4, no. 1 (2024): 32–50. http://dx.doi.org/10.55375/joerp.2024.4.4.

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In the realm of English Foreign Language (EFL) learning, the significance of vocabulary acquisition has been repeatedly underscored. Despite its paramount importance, memorizing words has remained a formidable challenge. This research, drawing on the outcomes in educational neuroscience, introduces the AEFC Method based on generative artificial intelligence (AI). The AEFC Method comprises four components, harnessing AI to generate learning materials for, 1) capturing students' attention by AI-generated content containing familiar elements for students, 2) fostering active engagement with AI su

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Guo, Hui, and Syaza Hazwani Zaini. "Artificial Intelligence in Academic Writing: A Literature Review." Asian Pendidikan, no. 4 (November 25, 2024): 46–55. https://doi.org/10.53797/aspen.v4i2.6.2024.

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Artificial intelligence (AI) has emerged as a transformative technology in education. This review focused on the intersection of AI tools and academic writing, addressing challenges such as plagiarism, language barriers, and feedback processes. The problem statement revolved around the increasing integration of AI in academic contexts, which offered opportunities for improved student learning but raised concerns over ethical issues such as plagiarism and over-dependence on AI-generated content. The purpose of this review was to critically review highly cited studies on the use of AI in academi

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