Relevant bibliographies by topics / AI Feedback

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI Feedback'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "AI Feedback"

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Li, Xinyue, Mingpeng Huang, Jialin Liu, Yifan Fan, and Min Cui. "The Impact of AI Negative Feedback vs. Leader Negative Feedback on Employee Withdrawal Behavior: A Dual-Path Study of Emotion and Cognition." Behavioral Sciences 15, no. 2 (2025): 152. https://doi.org/10.3390/bs15020152.

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In the workplace, the application of artificial intelligence (AI) is becoming increasingly widespread, including in employee performance management where AI feedback is gaining importance. Some companies are also using AI to provide negative feedback to employees. Our research compares the impact of AI negative feedback and leader negative feedback on employees. In order to explore the impact of AI negative feedback on employees, we investigated how AI negative feedback impacts employee psychology and behavior and compared these effects to those of human leader negative feedback, within the fr
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Alghannam, Manal Saleh M. "Artificial Intelligence as a Provider of Feedback on EFL Student Compositions." World Journal of English Language 15, no. 2 (2024): 161. https://doi.org/10.5430/wjel.v15n2p161.

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In response to the arrival of advanced artificial intelligence (AI) in the form of ChatGPT, this study examines its potential for providing feedback to foreign language writers. This represents a more acceptable use of AI in the writing classroom, rather than students simply using AI to write their entire essay. The methodological procedure involved eliciting normal classroom writing-practice essays from 29 English major students at a Saudi university, with ChatGPT (2023) then given a simple prompt requesting feedback. Both the essays and the feedback were qualitatively analysed to respond to
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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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Cheong, Yunam. "A Study of the Effectiveness of Student Perception-Based AI Feedback in College Writing Classes." Korean Association of General Education 18, no. 5 (2024): 159–73. http://dx.doi.org/10.46392/kjge.2024.18.5.159.

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This study examined the educational effectiveness of AI-based automated evaluation feedback tools in college writing courses. Amid active discussions on the use of AI in liberal arts education, the study applied AI automated evaluation tools to actual writing classes and analyzed the results. The findings show that AI feedback positively influenced students' motivation for writing and achieved a high level of satisfaction in areas such as spelling, vocabulary, grammar, and expression correction accuracy. AI automated evaluation feedback helps reduce the feedback burden on instructors and assis
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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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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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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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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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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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A.M. Chimanna(Umrani),. "EduConvo: AI-Driven Student Feedback System." Journal of Information Systems Engineering and Management 10, no. 53s (2025): 223–35. https://doi.org/10.52783/jisem.v10i53s.10861.

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Student feedback is a crucial tool for educational institutions to assess teaching effectiveness and improve course delivery. However, traditional feedback collection methods, e.g., static surveys, suffer from low engagement, vague responses, and a lack of actionable insights. To address these limitations, this paper presents a Conversational AI-based Student Feedback System that uses a Large Language Model (LLM) to facilitate dynamic, interactive, and adaptive feedback collection. The system personalizes questions based on course content, allowing in-depth responses while maintaining anonymit
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Dissertations / Theses on the topic "AI Feedback"

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Brown, Kristoffer, and Johannes David Liss Thessén. "Automated Feedback on Learning Progress : An exploratory study of opportunities and risks associated with automatically computed metrics and recommendations." Thesis, KTH, Lärande, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277098.

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For the past 100 years, attempts have been made to develop teaching machines that support students’learning. Judging by the contemporary increase in funding for educational technology companies,there is now more than ever an interest in technology that facilitates learning. As a result of studentsbeing able to interact with learning material via internet-connected devices, it has become feasible toanalyze large quantities of interactional data in order to provide learners with individually tailoredlearning paths. This is one of the services provided by the company Sana Labs. Their algorithms a
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Boutet, de Monvel Violaine. "Du feedback vidéo à l'IA générative : sur la récursivité dans les arts et médias." Electronic Thesis or Diss., Paris 3, 2025. http://www.theses.fr/2025PA030009.

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Cette thèse érige, sous le prisme du feedback, un pont entre l’art vidéo pionnier des années 1960 à 1980 et les pratiques en lien avec l’IA générative, que les avancées phénoménales de l’apprentissage profond ont précipitées depuis le milieu des années 2010. La rétroaction renvoie en cybernétique à l’autorégulation par la boucle de systèmes naturels et technologiques. Appliqué à des dispositifs analogiques, numériques ou hybrides en circuit fermé, ce processus automatisé qualifie aussi les effets contingents qui en résultent à l'écran. La première partie revient sur l’influence colossale que l
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Wåhlin, Peter. "Enhanching the Human-Team Awareness of a Robot." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16371.

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The use of autonomous robots in our society is increasing every day and a robot is no longer seen as a tool but as a team member. The robots are now working side by side with us and provide assistance during dangerous operations where humans otherwise are at risk. This development has in turn increased the need of robots with more human-awareness. Therefore, this master thesis aims at contributing to the enhancement of human-aware robotics. Specifically, we are investigating the possibilities of equipping autonomous robots with the capability of assessing and detecting activities in human team
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Books on the topic "AI Feedback"

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Harrasi, Nasser Hamed Al, and Mohamed Salah El Din. Utilizing AI for Assessment, Grading, and Feedback in Higher Education. IGI Global, 2024.

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Harrasi, Nasser Hamed Al, and Mohamed Salah El Din. Utilizing AI for Assessment, Grading, and Feedback in Higher Education. IGI Global, 2024.

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Harrasi, Nasser Hamed Al, and Mohamed Salah El Din. Utilizing AI for Assessment, Grading, and Feedback in Higher Education. IGI Global, 2024.

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Harrasi, Nasser Hamed Al, and Mohamed Salah El Din. Utilizing AI for Assessment, Grading, and Feedback in Higher Education. IGI Global, 2024.

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Harrasi, Nasser Hamed Al, and Mohamed Salah El Din. Utilizing AI for Assessment, Grading, and Feedback in Higher Education. IGI Global, 2024.

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Denham Smith, Dina, and Alicia A. Grandey. Emotionally Charged. Oxford University PressNew York, 2025. https://doi.org/10.1093/oso/9780197750155.001.0001.

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Abstract Leaders are confronted with more emotional demands than ever, due to changes in the way people work, who they work with, and why they work. Emotionally Charged provides a comprehensive guide to help leaders and those who support them appreciate and navigate these exceptional new emotional demands. Each chapter equips readers with evidence-based insights and tools for building and applying the advanced emotional skills for effective leadership today, from regulating emotions in yourself and others to navigating emotionally charged work events effectively. Throughout the book, the autho
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Book chapters on the topic "AI Feedback"

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Kore, Akshay. "Designing Feedback." In Designing Human-Centric AI Experiences. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8088-1_5.

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Trajkovski, Goran, and Heather Hayes. "AI-Assisted Formative Assessment and Feedback." In Digital Education and Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88252-4_7.

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Ao, Zhuoyun, Martin Oxenham, and Mark Nelson. "Classifiers for Regulatory Feedback Networks Using AB-Divergences." In AI 2020: Advances in Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64984-5_25.

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Ardimento, Pasquale, Mario Luca Bernardi, Marta Cimitile, and Michele Scalera. "A RAG-Enhanced AI Feedback for UML Education." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-99264-3_2.

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Qassrawi, Rania Muhammad. "Meta-Analysis of Using AI-Based Feedback Systems in Developing College Students’ Academic Writing Skills." In Sustainability, AI and Innovation: Proceedings of the Applied Research in Humanities & Social Sciences (ARHSS 2023). Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2532-1_20.

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Abstract In a technology-driven world where using artificial intelligence (AI) systems have dominated in all fields, educational institutions have become keen to embrace innovative tools and applications as a fundamental part of teaching-learning to promote learners’ skills and competencies. Automated writing feedback systems, which are AI-based tools, are being used excessively at universities to provide students with immediate and timely written feedback; hence, reviewing, analyzing, and synthesizing the methods and results of existing studies in this regard can be considered a crucial headw
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Wynn, Adam T., Jingyun Wang, Kaoru Umezawa, and Alexandra I. Cristea. "An AI-Based Feedback Visualisation System for Speech Training." In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11647-6_104.

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WU, Siegfried Zhiqiang. "The Technical Framework of AI Cities." In The Urban Book Series. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2560-4_8.

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Abstract This chapter delineates the technical framework of an AI-driven city and its pivotal components, with a particular emphasis on data perception and database management, intelligent feedback mechanisms, decision-making technologies, and their application scenarios. It provides an in-depth examination of the establishment of CiMA (Urban Intelligent Mapping Alliance) and the evolution of its database architecture, which facilitates large-scale urban data storage and processing nationwide, thereby fostering the collaborative construction and sharing of foundational digital city data. Regar
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Dewil, Sophie, Mingxiao Liu, Sean Sanford, and Raviraj Nataraj. "Neural Responses to Altered Visual Feedback in Computerized Interfaces Driven by Force- or Motion-Control." In AI Technologies and Virtual Reality. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9018-4_22.

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Lewis, Lise. "How artificial intelligence (AI) may impact on the future of feedback." In Relational Feedback. Routledge, 2020. http://dx.doi.org/10.4324/9781315160726-11.

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Shivshankar, Smitha, and Nirmal Acharya. "AI in Assessment and Feedback." In Advances in Educational Technologies and Instructional Design. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7220-3.ch006.

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This chapter examines AI's transformative role in educational assessment and feedback, focusing on enhancing teaching and learning through technology. As AI reshapes traditional practices, the chapter emphasizes that AI in assessment should extend beyond automation, advocating for designs that encourage deep learning and student engagement. Using adaptive feedback, educators can support skill development and promote meaningful engagement with the material. The chapter presents a framework for AI-integrated assessments, stressing clear guidance, ethical considerations, and the active roles of b
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Conference papers on the topic "AI Feedback"

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Hua, Yuqing, Jiajia Guo, Chao-Kai Wen, Chengyong Jiang, and Shi Jin. "CQI Generation for AI-Enabled CSI Feedback." In 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2024. https://doi.org/10.1109/wcsp62071.2024.10826731.

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Babushkin, Konstantin, Nikolai Timofeev, and Ivan Filianin. "Research on AI-Powered Employee Feedback Tools." In 2025 International Russian Smart Industry Conference (SmartIndustryCon). IEEE, 2025. https://doi.org/10.1109/smartindustrycon65166.2025.10986092.

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Gao, David, Ian Miller, Ali Allami, and Dan Lin. "Preserving Privacy During Reinforcement Learning With AI Feedback." In 2024 IEEE 6th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA). IEEE, 2024. https://doi.org/10.1109/tps-isa62245.2024.00033.

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Mishra, Abhishek, Siddharth Singh, and R. C. Jisha. "AI-Powered Model for Intelligent Resume Recommendation and Feedback." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726016.

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Mukhtorova, Barno, Mirdjalol Mirzaakhmedov, Malika Ganiyeva, Umidakhon Usmonova, Zakhro Mamadaliyeva, and Mukhammadali Samindjonov. "AI in Education: Redefining Language Assessment and Feedback Mechanisms." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923438.

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Williamson, Ashley, and John Murray. "“HEY AI! TRANSLATE MY PROFESSOR? THANKS!”: DESIGNING AN AI TUTOR TO TALK WITH YOUR FEEDBACK." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1055.

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Towle, Benjamin, and Ke Zhou. "Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.705.

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Odesola, Oluwasegun, Hiba Alsmadi, Ala S. Al Kafari, and Gok Kandasamy. "AI-Powered Instant Textual Feedback on Physiotherapist Student Practical Perfomance." In 2024 Intelligent Methods, Systems, and Applications (IMSA). IEEE, 2024. http://dx.doi.org/10.1109/imsa61967.2024.10652614.

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Navarro, Luis Virgen, María Magdalena González Pérez, Paloma Barajas-Álvarez, Emiliano Ortiz-Zavala, Ernesto Reyes Villegas, and José Miguel Sánchez-Lizárraga. "Qualitative Feedback Comparison Between Professors and AI in STEM Education." In 2025 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2025. https://doi.org/10.1109/educon62633.2025.11016507.

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Lindsay, Euan D., Mike Zhang, Aditya Johri, and Johannes Bjerva. "The Responsible Development of Automated Student Feedback with Generative AI." In 2025 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2025. https://doi.org/10.1109/educon62633.2025.11016572.

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Reports on the topic "AI Feedback"

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Gehlhaus, Diana. Staying Ahead: Strengthening Tomorrow's U.S. AI and AI-Enabled Workforce. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20210075.

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This research agenda provides a roadmap for the next phase of CSET’s line of research on the U.S. AI workforce. Our goal is to assist policymakers and other stakeholders in the national security community to create policies that will ensure the United States maintains its competitive advantage in AI talent. We welcome comments, feedback and input on this vision at cset@georgetown.edu.
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Seals, Corinne. AI Tools for Academic Publications. Instats Inc., 2025. https://doi.org/10.61700/57c61kcgcz6821981.

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This seminar provides a comprehensive workshop on writing academic journal articles, led by expert Corinne Seals, focusing on strategies to successfully navigate the publication process. Participants will acquire essential skills for crafting compelling manuscripts, leveraging technology in their writing, and addressing reviewers' feedback effectively.
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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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Dubey, Manvendra, James Benedict, Daniel O'Malley, et al. AI for Extreme Volcanic Climate Forcing and Feedback Forecasting in the 21st century. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1765859.

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Dubey, Manvendra, James Benedict, Daniel O'Malley, et al. AI for Extreme Volcanic Climate Forcing and Feedback Forecasting in the 21st century. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769659.

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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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Pautz Stephenson, Stefani, Rebecca Banks, and Merijke Coenraad. Outcomes of Increased Practitioner Engagement in Edtech Development: How Strong, Sustainable Research-Practice-Industry Partnerships will Build a Better Edtech Future. Digital Promise, 2022. http://dx.doi.org/10.51388/20.500.12265/158.

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A new participatory research model, Research-Practice-Industry Partnerships (RPIP), presents a unique value proposition. Design processes have typically placed professional designers, not the end users, at the center of the work. RPIPs create an intentional feedback loop that transforms the knowledge, action, or goals of all involved parties (Baker et al., 2022). RPIP aims to create better designs for scalable technologies that both meet the needs of educators and incorporate research from the learning sciences. This yields a product more likely to be used, used appropriately, and have the des
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Bonnett, Michaela, Meaghan Kennedy, Sophie Becker, and Odiraa Okala. A Cross-Sectoral Approach to Innovation in Public Health. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/hbtw2644.

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Brief Description Innovative methods need to be integrated into public health thinking to accelerate change, a belief that led to the creation of our public health innovation cohorts. 11 participants were taught new ways of approaching problems and how to test with pilots. Thematic and network analysis of interviews, along with a 2-year follow-up, revealed an enduring shift in approach to problem-solving within their work. Background Public health tends to be siloed and reactive in solving new and existing problems. Innovation and the development of novel approaches requires cross-sectoral col
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ARL/CNI AI Scenarios: Strategic Context. Association of Research Libraries, Coalition for Networked Information, and Stratus Inc., 2024. http://dx.doi.org/10.29242/report.aiscenarioscontext2024.

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This strategic context report summarizes community feedback gathered through focus groups and interviews about an AI-influenced future for the research environment that were held in winter 2023–24 and spring 2024. This report is part of the Deluxe Edition of the ARL/CNI AI Scenarios: AI-Influenced Futures. This publication is a critical asset for research libraries and organizations aiming to strategically plan for the transformative power of artificial intelligence. This edition is designed to empower stakeholders across the research enterprise by providing them with the tools and knowledge n
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