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Journal articles on the topic 'AI Feedback'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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’ 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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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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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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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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Youn, Chu Hui, Abdul Rahim Salam, and Aida A. Rahman. "AI-Driven Tools in Providing Feedback on Students’ Writing." International Journal of Research and Innovation in Social Science IX, IIIS (2025): 58–67. https://doi.org/10.47772/ijriss.2025.903sedu0006.

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Artificial Intelligence (AI) has seen substantial progress in recent years, establishing itself as a crucial element in diverse sectors, particularly education. Providing feedback is one of the key areas where AI has made a notable impact, and it is essential for fostering students’ learning, particularly in developing writing skills. Feedback in writing represents a critical component of effective teaching practices, and AI introduces innovative approaches to enhance its delivery and impact. This systematic review rigorously examines the existing body of literature on the use of AI in educati

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Zhang, Yuze, Haojie Li, and Rui Huang. "The Effect of Tai Chi (Bafa Wubu) Training and Artificial Intelligence-Based Movement-Precision Feedback on the Mental and Physical Outcomes of Elderly." Sensors 24, no. 19 (2024): 6485. http://dx.doi.org/10.3390/s24196485.

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(1) Background: This study aims to compare the effects of AI-based exercise feedback and standard training on the physical and mental health outcomes of older adults participating in a 4-week tai chi training program. (2) Methods: Participants were divided into three groups: an AI feedback group received real-time movement accuracy feedback based on AI and inertial measurement units (IMUs), a conventional feedback group received verbal feedback from supervisors, and a control group received no feedback. All groups trained three times per week for 8 weeks. Outcome measures, including movement a

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Lee, Seung Chul, Sun Hyun Moon, and Soo Rim Noh. "The Influence of Visual Feedback from AI speakers on Continued Usage Intention among Younger and Middle-aged Users: The Mediating Roles of Emotional Experience and Product Attitude." Korean Data Analysis Society 25, no. 3 (2023): 1161–75. http://dx.doi.org/10.37727/jkdas.2023.25.3.1161.

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Artificial intelligence (AI) speakers are widely used in smart home environments, but user satisfaction is low, which can discourage user intention to continue using the device. To address this, researchers have suggested adding visual feedback to AI speakers. This study examined the effect of visual feedback on AI speaker usage intention and to verify the mediating effects of emotional experience and product attitude in younger and middle-aged adults. Videos of AI speaker disconnection situations were filmed, half of the participants were shown the videos (control group). The other half watch

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Malik, Sumayyah, and Salma Kalim. "AI-Assisted Feedback Bots in Pakistani ESP Classrooms: Improving Writing Proficiency and Learner Autonomy." Al-Qantara 11, no. 1 (2025): 140–61. https://doi.org/10.5281/zenodo.14967881.

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Purpose: This research explored the potential of the integration of AI-assisted feedback bots as a tool to enhance the writing proficiency of undergraduate ESP learners in Pakistan. Method: Utilizing a Mixed Methods Action Research approach, this study evaluated the impact of AI-assisted feedback bots on writing proficiency, demonstrated by the substantial increase in post-test scores. The qualitative content analysis highlighted the AI bot’s role in providings tructured guidance, task-specific assistance and promoting learner autonomy, though some students preferred traditional feedback

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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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E. Duraimurugan, S. Kesava Sundara Nathan, and V. Vishnuvardhan. "Persona: Revolutionizing Conversations with AI Automation." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 831–36. https://doi.org/10.47392/irjaem.2025.0134.

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Implementing adaptive learning mechanisms improves the accuracy of the detection process by continuously refining its capabilities based on real-time user feedback. A website is designed to focus on interactive pathways, bot management, easy UI, and support. This approach ensures that the system evolves, learning from past interactions to deliver more accurate and reliable results. By efficiently managing bulk calls for regular feedback collection, an AI-powered call automation bot significantly reduces the burden on human operators. This automation minimizes manual intervention, ensuring cons

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Campos, Miguel. "AI-assisted feedback in CLIL courses as a self-regulated language learning mechanism: students’ perceptions and experiences." European Public & Social Innovation Review 10 (January 31, 2025): 1–14. https://doi.org/10.31637/epsir-2025-1568.

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Introduction: The integration of AI in educational settings offers significant potential for enhancing learning experiences, particularly in Content and Language Integrated Learning (CLIL) contexts. AI tools, such as ChatGPT, provide personalized feedback on writing, addressing issues like unclear content, grammatical errors, or poor vocabulary. This study examines students' perceptions of AI-assisted feedback in a business CLIL course and evaluates the actual improvements in their writing based on the feedback provided by AI. Methodology: University students (n=205) participated in a 15-week

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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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Drewery, David, Jennifer Woodside, and Kristen Eppel. "Artificial Intelligence and Résumé Critique Experiences." Canadian Journal of Career Development 21, no. 2 (2022): 28–39. http://dx.doi.org/10.53379/cjcd.2022.338.

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Where résumés are concerned, student supports tend to include tactical feedback that addresses issues in students’ writing and strategic feedback aimed at coaching critical self-reflection. However, there is not always time to cover all that could be offered by both kinds of feedback in a single résumé critique. Given demands on staff time, many career services administrators are considering opportunities to leverage artificial intelligence-based (AI) products that might offer tactical feedback and allow staff to focus on offering strategic feedback. In a field experiment, we explored how novi

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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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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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Bian, Yanyan, Yongbo Xiang, Bingdu Tong, Bin Feng, and Xisheng Weng. "Artificial Intelligence–Assisted System in Postoperative Follow-up of Orthopedic Patients: Exploratory Quantitative and Qualitative Study." Journal of Medical Internet Research 22, no. 5 (2020): e16896. http://dx.doi.org/10.2196/16896.

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Background Patient follow-up is an essential part of hospital ward management. With the development of deep learning algorithms, individual follow-up assignments might be completed by artificial intelligence (AI). We developed an AI-assisted follow-up conversational agent that can simulate the human voice and select an appropriate follow-up time for quantitative, automatic, and personalized patient follow-up. Patient feedback and voice information could be collected and converted into text data automatically. Objective The primary objective of this study was to compare the cost-effectiveness o

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Choi, Jin-Young. "Generative AI-based Writing Feedback Model Development." Korean Association for Literacy 15, no. 5 (2024): 13–62. http://dx.doi.org/10.37736/kjlr.2024.10.15.5.01.

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This study aimed to develop a systematic writing feedback model that reduces the burden on teachers in the writing process while allowing students to receive individualized feedback using generative AI. Thirteen experts were recruited and a Delphi survey was conducted. The results were as follows. First, we identified a writing process for utilizing generative AI and a feedback structure for that process. Second, we finalized the argumentative writing test and its evaluation factors and detailed scoring criteria to be applied to this model. Third, we added examples of generative AI question ty

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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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Zhang, Jie, and Jiashi Wang. "Student perceptions of hybrid feedback: Using Gen-AI to enhance engagement with EAP writing feedback." JALT CALL Journal 21, no. 2 (2025): 2175. https://doi.org/10.29140/jaltcall.v21n2.2175.

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Feedback is crucial in the learning process, yet many students struggle to effectively engage with teacher feedback. This study explores the potential of Generative Artificial Intelligence (Gen-AI) to enhance student engagement in EAP writing feedback. Using a mixed-methods approach that includes surveys and interviews with Year 1 and Year 2 students at a Sino-foreign EMI university, the research examines students’ cognitive, behavioral, and affective engagement with feedback, focusing on how Gen-AI tools like ChatGPT can help clarify and implement feedback. The findings suggest that integrati

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Sumedi, Siti Hanna. "AI-Powered Mediated Synchronous Corrective Feedback on Efl Senior High School Students’ Paragraph Writing Skill." EDULIA: English Education, Linguistic and Art Journal 5, no. 1 (2024): 12–22. https://doi.org/10.31539/edulia.v5i1.11113.

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This study investigated the effect of AI-powered mediated Synchronous Corrective Feedback as an AI-based automated pedagogical aid on senior high school students’ English writing skill. Correspondingly, this study was conducted through quantitative methods, specifically quasi-experimental research design. There were 30 eleventh-grade students as research subjects who were assigned into two different classes. Practically, 15 students were sorted to an experimental class in which implemented AI-powered mediated Synchronous Corrective Feedback supported by the teacher’ corrective feedback in term

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Duong, Thi Kim Hue, and Thi Thu Huong Le. "Utilizing Artificial Intelligence in Writing Feedback: Benefits and Challenges for First-Year Students at Hanoi University of Industry." Proceedings of the AsiaCALL International Conference 6 (December 23, 2024): 238–49. https://doi.org/10.54855/paic.24617.

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This paper analyses the application of AI technology to the process of delivering writing feedback to first-year students of Hanoi University of Industry (HaUI). AI brings an effective solution to improve writing skills since one wants to receive individual and effective instruction. In this article, the authors discuss how there are opportunities for the use of AI to improve feedback quality, time, and flexibility in line with the various AI tools and platforms that cater to young writers. It also explores the implications and difficulty of incorporating AI-driven feedback systems for the cla

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Lee, Juyeong, and Hunkoog Jho. "The Impact of an AI-based Feedback System on the Improvement of Elementary Students’ Statistical Inquiry Question Posing: The Moderating Effects of AI Perception and Feedback Self-efficacy." Brain, Digital, & Learning 14, no. 3 (2024): 459–73. http://dx.doi.org/10.31216/bdl.20240026.

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With the advent of the digital age, the amount of data has exponentially increased making statistical analysis essential for decision-making and problem-solving. This research examines the impact of an AI-based feedback system (FS) using the GPT-4 model on the ability of sixthgrade students to formulate statistical inquiry questions. Conducted at an elementary school in Gyeonggi-do, the research involved 95 students divided into experimental and control groups, who participated in an eight-session program. The experimental group received feedback from the AI-based FS, while the control group r

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Cardu, Clare. "Analysing parent feedback." British Journal of Midwifery 30, no. 1 (2022): 6–7. http://dx.doi.org/10.12968/bjom.2022.30.1.6.

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Prof. Anuja Garande, Kushank Patil, Rasika Deshmukh, Siddhi Gurav, and Chaitanya Yadav. "AI Trainer : Video-Based Squat Analysis." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 2 (2024): 172–79. http://dx.doi.org/10.32628/ijsrset2411221.

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This research proposes a video-based system for analyzing human squats and providing real-time feedback to improve posture. The system leverages MediaPipe, an open-source pose estimation library, to identify key body joints during squats. By calculating crucial joint angles (knee flexion, hip flexion, ankle dorsiflexion), the system assesses squat form against established biomechanical principles. Deviations from these principles trigger real-time feedback messages or visual cues to guide users towards optimal squat posture. The paper details the system architecture, with a client-side applica

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Kurt, Gokce, and YAVUZ KURT. "Enhancing L2 Writing Skills: ChatGPT as an Automated Feedback Tool." Journal of Information Technology Education: Research 23 (2024): 024. http://dx.doi.org/10.28945/5370.

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Aim/Purpose: The present study explores the use of AI-powered ChatGPT as a feedback tool for automated writing evaluation in a higher education context. Background: Artificial intelligence (AI) is increasingly applied in the field of education, offering new opportunities with its evolving capabilities. One area where AI tools have promising potential is the field of second/foreign language (L2) writing. Methodology: Participants, Turkish pre-service teachers of English enrolled in an academic writing course, received feedback from ChatGPT, peers, and the course instructor. Data came from focus

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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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Rad, Dana, Alina Felicia Roman, Daniel Mara, et al. "Exploring AI integration in education: a sequential mediation analysis." Journal of Educational Sciences & Psychology 15 (76), no. 2 (2024): 167–76. https://doi.org/10.51865/jesp.2024.2.15.

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This study explores the psychological impact of AI technology on students' recognition of educational benefits from AI integration, mediated by variation of the psychological influence of AI technology, benefits of rapid feedback provided by AI in the learning process, and perception of AI involvement in career and education. Conducted across six Romanian universities with 675 valid responses, it investigates a sequential mediation model linking AI technology's psychological influence, feedback benefits, career perceptions, and Benefits of AI integration in education. Results reveal a positive

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OLABODE, Adeyinka Ayoola, and James Adekunle ADEDOKUN. "EVALUATING THE USAGE AND IMPACT OF ARTIFICIAL INTELLIGENCE FOR REAL-TIME FEEDBACK ON ENGLISH LANGUAGE TASKS AND STUDENT'S PERFORMANCE IN COLLEGES OF EDUCATION." Beyond Babel: BU Journal of Language, Literature and Humanities 9, no. 1 (2025): 140–52. https://doi.org/10.5281/zenodo.15427604.

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<em>This study assessed the effectiveness of AI-driven real-time feedback on students' performance in English language tasks at the Federal College of Education (Technical) Akoka. Specifically, the study evaluated the impact of such feedback on academic performance and to investigate students' perceptions, motivation, and engagement levels. Four research questions guided this inquiry, focusing on performance improvements, student perceptions of feedback, the effect on motivation and self-efficacy, and the relationship between feedback frequency and engagement. Employing a quasi-experimental re

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Yu, Tianshu, Ting-En Lin, Yuchuan Wu, Min Yang, Fei Huang, and Yongbin Li. "Diverse AI Feedback For Large Language Model Alignment." Transactions of the Association for Computational Linguistics 13 (2025): 392–407. https://doi.org/10.1162/tacl_a_00746.

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Abstract Recent advances in large language models (LLMs) focus on aligning models with human values to minimize harmful content. However, existing methods often rely on a single type of feedback, such as preferences, annotated labels, or critiques, which can lead to overfitting and suboptimal performance. In this paper, we propose Diverse AIFeedback (DAIF), a novel approach that integrates three types of feedback—critique, refinement, and preference—tailored to tasks of varying uncertainty levels. Through an analysis of information gain, we show that critique feedback is most effective for low

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Karishma, Nunna, Nikhil Raj Yammani, Sri Harsha B, Prithwee Reddei, and Venkataravana Nayak. "Personal AI Trainer." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4361–66. http://dx.doi.org/10.22214/ijraset.2023.52637.

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Abstract: Incorporating regular exercise into these daily routines has grown more difficult in today's fast-paced environment. To maintain perfect form and avoid injuries, training must include individualized coaching and feedback. However, it can be challenging for people to attend regular gym sessions or hire personal trainers due to the limitations of time, money, and accessibility. The ongoing global epidemic has also made it harder for people to access physical exercise centres and in-person instruction. This provides an original solution called the AI Fitness Tracker to address these iss

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Aggarwal, Deepshikha, Deepti Sharma, and Archana B. Saxena. "Adoption of Artificial Intelligence (AI) For Development of Smart Education as the Future of a Sustainable Education System." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 36 (October 17, 2023): 23–28. http://dx.doi.org/10.55529/jaimlnn.36.23.28.

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Adoption of artificial intelligence (AI) for development of Smart education as the future of a sustainable education system is gaining momentum worldwide. AI can transform the way we teach and learn, making education more personalized and efficient. With AI, adaptive learning platforms can analyse students' strengths and weaknesses, tailoring lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance. AI can also assist in content creation and assessment, automating tasks like grading and feedback. By integrating AI into education, we

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Sadji Evenddy, Sutrisno. "Investigating AI's Automated Feedback in English Language Learning." FLIP Foreign Language Instruction Probe 3, no. 1 (2024): 76–87. http://dx.doi.org/10.54213/flip.v3i1.401.

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This literature review examines the role of artificial intelligence (AI) in enhancing feedback mechanisms within English language learning, emphasizing the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and speech recognition to provide real-time, personalized feedback. The purpose of this review is to synthesize existing research findings on the effectiveness of AI-driven feedback in improving language skills such as grammar, vocabulary, and pronunciation, while also assessing learner engagement and retention rates. A thorough methodology invo

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Bhardwaj,, Atul. "Feedback Forge A Tool to Evaluate Your Answer Using AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35423.

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Feedback Forge is an innovative online platform designed to enhance users' writing skills through personalized feedback. Traditional feedback mechanisms often struggle to provide timely and relevant guidance, hindering skill development. In response, Feedback Forge leverages advanced technologies, including artificial intelligence and machine learning, to deliver tailored feedback at scale. This paper explores the landscape of feedback mechanisms, highlighting the limitations of traditional methods and the potential of technology-driven solutions. Key themes include the importance of feedback

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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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Bar, Ayush Kumar, and Avijit Kumar Chaudhuri. "Emotica.AI - A Customer feedback system using AI." International Research Journal on Advanced Science Hub 5, no. 03 (2023): 103–10. http://dx.doi.org/10.47392/irjash.2023.019.

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Micha, Evi. "Axioms for AI Alignment from Human Feedback." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 28722. https://doi.org/10.1609/aaai.v39i27.35116.

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In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory. From this perspective, we can evaluate different aggregation methods via established axioms, examining whether these methods meet or fail well-known standards. We demonstrate that both the Bradley-Terry-Luce Model and its broad generali

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Feshchenko, Artem Viktorovich, Irina Yur'evna Malkova, and Kristina Igorevna Buyakova. "AI-feedback in education: user experience analysis." Педагогика и просвещение, no. 3 (March 2025): 23–40. https://doi.org/10.7256/2454-0676.2025.3.75129.

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The subject of the study is the subjective experience and perception of students' feedback system based on generative artificial intelligence (AI) in real conditions of the educational process. The aim of the work was to identify the key advantages and disadvantages of the AI-assessor technology and to determine the factors affecting the trust and satisfaction of its users. The research methodology is based on a qualitative approach using combined data analysis. The empirical base of the study: 69 spontaneous user feedbacks obtained during the pilot implementation of the “AI-evaluator” system

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Rainey, Clare, Raymond Bond, Jonathan McConnell, et al. "The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography." PLOS One 20, no. 5 (2025): e0322051. https://doi.org/10.1371/journal.pone.0322051.

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Artificial intelligence decision support systems have been proposed to assist a struggling National Health Service (NHS) workforce in the United Kingdom. Its implementation in UK healthcare systems has been identified as a priority for deployment. Few studies have investigated the impact of the feedback from such systems on the end user. This study investigated the impact of two forms of AI feedback (saliency/heatmaps and AI diagnosis with percentage confidence) on student and qualified diagnostic radiographers’ accuracy when determining binary diagnosis on skeletal radiographs. The AI feedbac

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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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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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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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Reseacher. "LEVERAGING GENERATIVE AI FOR EFFICIENT MOBILE APP FEEDBACK CLASSIFICATION AND QUALITY IMPROVEMENT." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 444–51. https://doi.org/10.5281/zenodo.13285291.

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This paper introduces an innovative approach to mobile app user feedback analysis by harnessing the power of Generative AI technologies. We present an integrated system architecture that seamlessly combines an API-based scheduler for comprehensive data collection, a cutting-edge Generative AI classifier for nuanced feedback categorization, robust database integration for efficient data management, and an interactive visualization module for actionable insights. Our system goes beyond traditional classification methods by not only categorizing feedback based on predefined themes but also dynami

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