Academic literature on the topic 'AI-generated feedback'

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Journal articles on the topic "AI-generated feedback"

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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&rsquo; 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&rsquo; 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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Book chapters on the topic "AI-generated feedback"

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Venzke, Jan, Richard Hohmann, Arno Krombholz, Petra Platen, and Markus Reichert. "Enhancing Learning Experiences in Sports Science through Video and AI-generated Feedback." In Learning Analytics und Künstliche Intelligenz in Studium und Lehre. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-42993-5_5.

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Zhu, Qi, Qian Bao, Na Liang, and Ziqi Liu. "Evaluating the Secondary Emotional Impact of Traditional and AI-Generated Artwork Outcomes Through Emotional Feedback." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93418-6_8.

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Nikolakis, Nikolaos, Paolo Catti, and Kosmas Alexopoulos. "An Explainable Active Learning Approach for Enhanced Defect Detection in Manufacturing." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_5.

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Abstract Artificial Intelligence (AI) can significantly support manufacturing companies in their pursuit of operational excellence, by maintaining efficiency while minimizing defects. However, the complexity of AI solutions often creates a barrier to their practical application. Transparency and user-friendliness should be prioritized to ensure that the insights generated by AI can be effectively applied in real-time decision-making. To bridge this gap and foster a collaborative environment where AI and human expertise collectively drive operational excellence, this paper suggests an AI approa
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Karagiannis, Panagiotis, Panagiotis Angelakis, and Sotiris Makris. "On an AI-Based System Architecture to Generate Robotic Cells in VR Environments." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_21.

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Abstract This paper focuses on the design and implementation of an AI-based architecture diagram. Each module of this diagram contributes to the generation and programming of different components, namely robot, fixtures, grippers, fences etc. that are necessary to create a robotic cell in virtual environments. Additionally, the application includes modules that perform self-correcting suggestions, checking for any anomalies, such as overlapping parts, in the generated scene, providing corrective feedback to the user. The aim of the architecture is to help with the creation of an AI-based appli
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Del Rio, Mauro, Luca Lianas, Oskar Aspegren, et al. "AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_48.

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AbstractWhile studies in pathology are essential for the progress in the diagnostic and prognostic techniques in the field, pathologist time is becoming an increasingly scarce resource, and can indeed become the limiting factor in the feasibility of studies to be performed. In this work, we demonstrate how the Digital Pathology platform by CRS4, for supporting research studies in digital pathology, has been augmented by the addition of AI-based features to accelerate image examination to reduce the pathologist time required for clinical studies. The platform has been extended to provide comput
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Nussbaum, Jonathan, Tatjana Legler, and Martin Ruskowski. "CPPM Copilot: Proposing an AI-Based Assistant for Manual Assembly Tasks in a Flexible Production." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_9.

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Abstract With an increase in flexibility in industries adopting the tenets of shared, skill-based, and modular production, a higher variability of products a single manufacturer offers can be anticipated. An immediate consequence is a potential inability to instruct assembly personnel in an adequate and detailed manner. This problem would primarily affect small and medium enterprises engaged in individual and small series productions. To face the issue, this paper examines the suitability of facilitating an AI assistant to support workers handling an ever-expanding range of assembly tasks. Thi
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Alzahrani, Ahmad, and Ying Zheng. "Exploring AI Applications in Essay-Based Assignments: Affordances and Risks." In AI - Ethical and Legal Challenges [Working Title]. IntechOpen, 2024. https://doi.org/10.5772/intechopen.1008230.

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This study examined the feasibility of employing artificial intelligence (AI) for feedback provision on essay-based assignments in a UK Higher Education setting. Although the critical role of feedback in enhancing students’ learning experiences is widely recognised, resource limitations and large student numbers often hinder its quality and timely delivery. Through in-depth interviews with four participants from a university in the UK, this research investigated AI applications in essay evaluation, utilising data from 12 AI-generated essays and their corresponding feedback. The aims of the stu
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Sherson, Jacob, and Florent Vinchon. "Facilitating Human Feedback for GenAI Prompt Optimization." In HHAI 2024: Hybrid Human AI Systems for the Social Good. IOS Press, 2024. http://dx.doi.org/10.3233/faia240230.

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This study investigates the optimization of Generative AI (GenAI) systems through human feedback, focusing on how varying feedback mechanisms influence the quality of GenAI outputs. We devised a Human-AI training loop where 32 students, divided into two groups, evaluated AI-generated responses based on a single prompt. One group assessed a single output, while the other compared two outputs. Preliminary results from this small-scale experiment suggest that comparative feedback might encourage more nuanced evaluations, highlighting the potential for improved human-AI collaboration in prompt opt
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Sholapurapu, Prem Kumar, and Munawar Y. Sayed. "AI-Driven Student Feedback Systems: Implementing Machine Learning Models for Personalized Assessment and Learning Pathways." In Artificial Intelligence-Powered Learning Analytics and Student Feedback Mechanisms for Dynamic Curriculum Enhancement and Continuous Quality Improvement in Outcome-Based Education. RADemics Research Institute, 2025. https://doi.org/10.71443/9789349552531-06.

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The integration of AI in education has revolutionized student assessment and feedback systems, enabling personalized learning pathways tailored to individual needs. The opacity of AI-driven feedback mechanisms presents significant challenges in transparency, trust, and pedagogical alignment. XAI has emerged as a critical solution to enhance interpretability, ensuring that students and educators can understand, validate, and act upon AI-generated assessments. This chapter explores cutting-edge techniques for explainable AI in student feedback systems, including attention mechanisms in NLP, SHap
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Lesner, Jasmine, Luke Murayama, Tony Guizar, Poom Phunjamaneechot, and Daniel Shapiro. "AI Personalized Interactive Fiction for Young Children." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241074.

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This paper presents a novel high-fidelity prototype designed to enable young children to create personalized, interactive stories using advanced AI technologies. Our approach addresses a gap in existing AI-driven storytelling applications by focusing specifically on young children. The prototype allows children to select story topics using emojis and engages them with vivid multimedia elements including AI-generated illustrations, sounds, and music. Our goal is to foster critical thinking through choice-based narratives that allow children to influence story outcomes. Parents can review and pr
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Conference papers on the topic "AI-generated feedback"

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Liao, Dinuo, James D. Lomas, and Cehao Yu. "Enhancing the aesthetic appeal of AI-generated physical product designs through LoRA fine-tuning with human feedback." In International Conference on AI-generated Content (AIGC 2024), edited by Duoqian Miao and Feng Zhao. SPIE, 2025. https://doi.org/10.1117/12.3066095.

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Peng, Yun, Akhilesh Deepak Gotmare, Michael R. Lyu, Caiming Xiong, Silvio Savarese, and Doyen Sahoo. "PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback." In 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge). IEEE, 2025. https://doi.org/10.1109/forge66646.2025.00008.

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Shin, Insub, Su Bhin Hwang, Yun Joo Yoo, Sooan Bae, and Rae Yeong Kim. "Comparing Student Preferences for AI-Generated and Peer-Generated Feedback in AI-driven Formative Peer Assessment." In LAK '25: The 15th International Learning Analytics and Knowledge Conference. ACM, 2025. https://doi.org/10.1145/3706468.3706488.

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Sutherland, Steven C., Tiago Machado, Shruti Mahajan, et al. "Exploring the Role of AI-Generated Feedback Tangential to Learning Outcomes." In 2023 IEEE Conference on Games (CoG). IEEE, 2023. http://dx.doi.org/10.1109/cog57401.2023.10333239.

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Almutairi, Mohammed, Charles Chiang, Yuxin Bai, and Diego Gomez-Zara. "tAIfa: Enhancing Team Effectiveness and Cohesion with AI-Generated Automated Feedback." In CHIWORK '25: Proceedings of the 4th Annual Symposium on Human-Computer Interaction for Work. ACM, 2025. https://doi.org/10.1145/3729176.3729197.

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Hou, Xiaolan, Shuyan He, and Rongxiu Cuigong. "Learner Use of AI-Generated Feedback for Written Corrective Feedback in L2 Writing: Usefulness,User Proficiency and Attitude." In ICEMT 2024: 2024 The 8th International Conference on Education and Multimedia Technology. ACM, 2024. http://dx.doi.org/10.1145/3678726.3678767.

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Waked, Arifi. "Incorporating AI-Administered Personalized Feedback in an Inquiry-Based Learning EFL Writing Class." In Eleventh International Conference on Higher Education Advances. Editorial Universitat Politècnica de València (edUPV), 2025. https://doi.org/10.4995/head25.2025.19871.

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In an academic writing class using inquiry-based learning, ChatGPT-3.5 was used to provide personalized feedback to 95 female undergraduate native speakers of Arabic. Throughout the semester, students completed four essays by engaging in two weeks of student-led, in-class essay writing workshops, followed by entering their work into ChatGPT-3.5 with the prompt “correct my grammar.” Students were to complete reflections on the types of errors found and how to use this information to improve future writing. Students either submitted the required reflection or incorrectly provided an AI-generated
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Tzirides, Anastasia O., Gabriela C. Zapata, Patrick Bolger, Bill Cope, Mary Kalantzis, and Duane Searsmith. "Assessing AI-Generated Feedback Quality: Instructor Perspectives on Fine-tuned GenAI for Higher Education Student Writing." In 19th International Conference of the Learning Sciences (ICLS) 2025. International Society of the Learning Sciences, 2025. https://doi.org/10.22318/icls2025.126051.

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Choudhary, Ishita, Nitika Tyagi, Pratham Taneja, and Ronak Bhatia. "Enhancing Review Authenticity Using Transformers: Web Extension for Detecting AI-Generated Fake Reviews vs Human-Written Feedback." In 2023 3rd Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2023. http://dx.doi.org/10.1109/asiancon58793.2023.10270213.

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Li, Nuocheng, and Ang Li. "An AI-Enhanced Auto Parts Knowledge Platform with Real-Time Community Interaction using Flutter and Firebase." In 9th International Conference on Artificial Intelligence, Soft Computing And Applications. Academy & Industry Research Collaboration, 2025. https://doi.org/10.5121/csit.2025.150415.

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This research presents an AI-enhanced auto parts knowledge platform that integrates realtime community discussions and AI-generated insights to provide accurate and structured information about vehicle components [1]. The system combines an Auto Parts Information Hub, which uses Google Gemini AI to generate specifications, pricing, and environmental impact data, with a real-time Community Forum where users can exchange knowledge. A survey-based experiment with 10 participants assessed user satisfaction with the platform, revealing high engagement in discussions and strong appreciation for AI-d
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Reports on the topic "AI-generated feedback"

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Greene Nolan, Hillary, and Mai Chou Vang. Automated Essay Scoring in Middle School Writing: Understanding Key Predictors of Students’ Growth and Comparing Artificial Intelligence- and Teacher-Generated Scores and Feedback. Digital Promise, 2023. http://dx.doi.org/10.51388/20.500.12265/187.

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Providing feedback to students in a sustainable way represents a perennial challenge for secondary teachers of writing. Employing artificial intelligence (AI) tools to give students personalized and immediate feedback holds great promise. Project Topeka offered middle school teachers pre-curated teaching materials, foundational texts and videos, essay prompts, and a platform for students to submit and revise essay drafts with AI-generated scores and feedback. We analyze AI-generated writing scores of 3,233 7th- and 8th-grade students in school year 2021-22 and find that students’ growth over t
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Greene Nolan, Ph.D., Hillary, Merijke Coenraad, Ph.D., and Viki Young, Ph.D. Teaching Partner, Grading Assistant, Substitute Teacher: Three Ways Teachers Positioned an Artificial Intelligence Tool in Writing Instruction. Digital Promise, 2024. http://dx.doi.org/10.51388/20.500.12265/226.

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This study investigates how teachers understand and position AI tools in middle school writing instruction, drawing on 27 teacher interviews collected during a study called Project Topeka that used an interactive argumentative writing platform with AI-generated scores and feedback. Based on the interviews, we generate an initial theoretical framework of how teachers position AI tools — and therefore themselves — in their teaching. We found that some teachers leveraged AI as a “teaching partner” that provided insights to help enhance teaching and learning while remaining central to instruction
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Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

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The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
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