Relevant bibliographies by topics / AI-based feedback

Contents

  1. Journal articles
  2. Books
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'AI-based feedback'

Author: Grafiati
Published: 7 June 2025
Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'AI-based feedback.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "AI-based feedback"

1

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.

Full text
Abstract:
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 application performing pose estimation and feedback generation. The methodology outlines data collection with various squat variations, system development integrating MediaPipe, and evaluation through user testing with comparison to expert evaluations. Key features include real-time feedback and customizable thresholds for user adaptation. Potential applications encompass fitness training, physical therapy, and sports training. Finally, the paper explores future work possibilities like mobile integration, advanced feedback mechanisms, and machine learning for automatic threshold adjustments. This research offers a valuable tool for squat analysis, empowering users to achieve their fitness goals with proper form and reduced injury risk.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 types and prompts to provide concrete examples of the model. The implications are as follows. First, generative AI has the potential to drive self-reflective feedback from students during the writing process. Second, generative AI can reduce teacher fatigue and provide more personalized feedback. Finally, we propose a concrete feedback model for generative AI in education. However, this study had a limitation in that it did not confirm the effect through the application of a practical model. Therefore, we will continue to conduct follow-up research.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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 assists them in providing meaningful feedback to students. It is expected that AI-based automated writing feedback tools will contribute to fostering AI literacy. This case study is significant in that it offers insights for effectively incorporating AI automated evaluation feedback into college writing courses.
APA, Harvard, Vancouver, ISO, and other styles
4

Sailaja, Swetha. "AI-BASED BODY LANGUAGE ANALYSIS FOR INTERVIEW FEEDBACK." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03424.

Full text
Abstract:
ABSTRACT: Body language is a vital communication component, particularly in job interviews, where non-verbal cues significantly influence a candidate’s perception and evaluation. This project proposes an AI-powered system that analyzes candidates’ body language during interviews to deliver structured feedback. Leveraging computer vision and machine learning, the system evaluates facial expressions, gestures, posture, and eye contact to assess confidence, engagement, and professionalism. By processing video inputs, it extracts behavioral patterns and generates personalized, data-driven insights to help candidates improve their non-verbal communication. The system benefits job seekers, HR professionals, and training institutions by offering unbiased, automated feedback to identify strengths and areas for improvement—promoting more effective interview preparation and decision-making. Keywords: Body Language, Interview Feedback, Machine Learning, Computer Vision, Posture Analysis, Facial Expression Recognition.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
(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 accuracy, balance, grip strength, quality of life, and depression, were assessed before and after the training period. (3) Results: Compared to pre-training, all three groups showed significant improvements in movement accuracy, grip strength, quality of life, and depression. Only the AI feedback group showed significant improvements in balance. In terms of movement accuracy and balance, the AI feedback group showed significantly greater improvement compared to the conventional feedback group and the control group. (4) Conclusions: Providing real-time AI-based movement feedback during tai chi training offers greater health benefits for older adults compared to standard training without feedback.
APA, Harvard, Vancouver, ISO, and other styles
6

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

Full text
Abstract:
The main goal of this project is to create an advanced system that can accept questions submitted by users, produce AI-generated answers, and guarantee their accuracy by using an integrated validation method. This involves connecting to external web APIs that have access to trustworthy and authoritative sources, allowing the system to compare AI-generated responses with verified factual data in real time. If the AI-generated answer is accurate, the system will show a confirmation, providing users with assurance of its reliability. However, if the answer is incorrect, the system will flag the error and present the accurate response, addressing the issue of AI generating believable but factually inaccurate answers. In addition, the system records inaccurate responses to detect recurring error trends, which helps to enhance and refine the AI model over time. It also includes an interactive explanation tool that allows users to comprehend the validation process, promoting transparency in decision- making. To increase user involvement, the system can provide information about the origin of the correct answer and offer insights into differences between the AI-generated answers and the correct ones. Additionally, the system will have real-time alerts for critical errors, promptly notifying users when high-risk or sensitive topics are involved. The system's overall accuracy will be evaluated through a periodic review mechanism, which will offer feedback on performance enhancements. Additionally, user feedback will be incorporated into the system to continuously improve it and adapt to changing information sources. Moreover, the system will utilize AI-based learning algorithms to anticipate and prevent potential errors, thus enhancing response quality over time. Ultimately, the project's goal is to establish a reliable and user-friendly AI environment that fosters trust through real-time verification, transparency, continuous enhancement, and minimized risks of incorrect AI outputs.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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 Data Description writing course, using ChatGPT to receive specific criteria-based feedback on weekly compositions. Students revised their drafts based on this feedback before their submission. A survey (n=192) assessed their experiences and the perceived impact on writing skills and task efficiency. Additionally, a sample (n=336) of the writing compositions was coded and analyzed to evaluate linguistic enhancement. Results: Results indicate that students found AI feedback beneficial for improving writing skills and appreciated its immediacy and specificity. However, concerns were noted about the complexity and relevance of the feedback. Discussions: Despite these issues, students responded positively, showing significant improvement in content accuracy and linguistic proficiency. Conclusions: The study highlights the potential of AI tools and the need for refining AI feedback mechanisms.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 received teacher feedback. Pre- and post-tests measured the improvement in students’ statistical inquiry question levels. Additionally, the study analyzed how students’ self-efficacy regarding feedback and their perception of AI moderated the effectiveness of the FS. Results showed that the AI-based FS significantly improved the students’ ability to pose statistical inquiry questions compared to the control group. The study also found that a positive perception of AI enhanced the effectiveness of the FS, while self-efficacy regarding feedback did not show a significant impact. These findings suggest that AI-based FS can be an effective educational tool, particularly when students have a positive attitude toward AI. Future research should focus on developing fine-tuned AI-based FS capable of providing detailed feedback throughout all stages of statistical inquiry and investigate its effects on students with cognitive challenges in accepting feedback.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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 theory (SCT) supports the potential use of AI chatbots in the learning process as AI can provide students with instant guidance and support that fosters personalized, independent learning experiences. Similarly, Krashen’s second language acquisition theory (SLA) was found to support the hypothesis that AI use can enhance student learning by creating meaningful interaction in the target language wherein learners engage in genuine communication rather than focusing solely on linguistic form. To determine the reliability of AI-generated feedback, an analysis was performed on student writing. First, two rubrics were created by ChatGPT-3; AI then graded the papers, and the results were compared with human graded results using the same rubrics. The study concludes that e-Learning arning certainly has great potential; besides providing timely, personalized learning support, AI feedback can increase student motivation and foster learning independence. Not surprisingly, though, several caveats exist. It was found that ChatGPT-3 is prone to error and hallucination in providing student feedback, especially when presented with longer texts. To avoid this, rubrics must be carefully constructed, and teacher oversight is still very much required. This study will help educators transition to the new era of AI-assisted e-Learning by helping them make informed decisions about how to provide useful AI feedback that is underpinned by sound pedagogical principles.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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 research questions concerning the feedback’s consistency and credibility, and the extent to which it represented the different potential feedback types, based on a review of the extensive literature on the subject. Although superficially impressive, close examination revealed certain weaknesses to the AI feedback. For example, there was inconsistency in how the feedback was handled across essays, and some statements were not fully accurate regarding the respective text. In focus, the feedback was primarily accuracy-oriented, while even-handed in attention to content, organisation, and lower-level language matters, providing both positive and negative comments. However, there was a paucity of message-oriented communicative and explicit affective feedback. Like many teachers, ChatGPT was selective in terms of the feedback provided, but the decisions of what to address did not seem altogether motivated by criteria that an expert human feedback provider would consider. The main conclusion is that while AI feedback on writing practice is useful, it does require human monitoring by a teacher.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "AI-based feedback"

1

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

Full text
Abstract:
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 authors delve into critical leadership topics such as emotional labor, authenticity, stress and recovery, emotion regulation, and self-compassion. They offer evidence-based strategies and techniques for leaders to thrive in the new world of work. Their guidance includes managing difficult conversations like communicating difficult feedback or handling layoffs, supporting employees struggling with personal challenges and mental health, and building connection and trust virtually. They also share specific strategies for leveraging diversity and building inclusive teams, and managing the new tensions and stressors introduced by advanced technologies such as AI. Through a blend of scientific evidence and practical advice, this book equips leaders with the tools and insights needed to navigate the complex emotional landscape at work, fostering healthy and productive work environments in the 21st century.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "AI-based feedback"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 headway. Thus, the current study aimed at meta-analyzing the previous studies relatable to the usage of AI-based feedback systems to improve the academic writing skills of university students. Consequently, 35 studies published between 2015 and 2023 were analytically summarized and grouped based on the meta-analysis guidelines established by Cooper (Management Decision 36:493–502, 1998). The results revealed numerous benefits and some challenges of utilizing this technology. It was displayed, for example, that using AI-based feedback systems can significantly improve students’ academic writing skills, increase their engagement, promote self-regulation, trigger meta-cognitive writing skills, and provide personalized feedback that facilitates the learning process. Conversely, some challenges were highlighted, especially the overuse of such technologies that can lead to dependent and reliant learners, in addition to the failure of these systems to provide feedback on writings that necessitate higher-order thinking skills, meanwhile offering generic feedback that ignores individual differences. Accordingly, a model for the optimal use of such systems was suggested in the conclusions.
APA, Harvard, Vancouver, ISO, and other styles
3

Romero, Margarida, Petros Lameras, and Sylvester Arnab. "Affordances for AI-Enhanced Digital Game-Based Learning." In Palgrave Studies in Creativity and Culture. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55272-4_9.

Full text
Abstract:
AbstractThis chapter investigates the dynamic synergy between pedagogy, social dynamics, and technological developments in Digital Game-Based Learning (DGBL), which is enhanced by artificial intelligence (AI). The chapter navigates through educational modifications, learner profiling challenges, social enhancements, and technical considerations, emphasising AI's revolutionary influence. The topic expands on the critical interaction between learning analytics and machine learning, demonstrating AI's promise for personalised and adaptable DGBL experiences. The practical ramifications of real-time feedback in AI-driven DGBL are discussed, with the goal of providing timely instruction and encouraging positive behaviours. Finally, the chapter sheds light on the collaborative evolution of AI-enhanced education, providing useful insights for educators, instructional designers, and developers in creating optimised digital learning environments.
APA, Harvard, Vancouver, ISO, and other styles
4

Salazar Morales, Anasilvia, Jorge Yass, Trishala Jain, Jayson Nissen, Ben Van Dusen, and Evrim Baran. "Understanding Sentiment in User Feedback: Lexicon-Based vs. Generative AI Approaches." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93724-8_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Gratani, Francesca, Laura Screpanti, Lorella Giannandrea, David Scaradozzi, and Lorenza Maria Capolla. "Personalized Feedback in University Contexts: Exploring the Potential of AI-Based Techniques." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67351-1_30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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. Regarding intelligent feedback and decision-making technologies, the AI city achieves precise configuration of urban functional facilities through multi-agent simulations and collective intelligence algorithms, achieving spatial accuracy within 5 m × 5 m × 3 m, surpassing traditional urban simulation systems. Moreover, cloud-based decision chains and intelligent configuration decision technologies emulate biological neural transmission mechanisms to enhance the city’s responsiveness and governance capabilities. Additionally, the chapter explores the specific implementation of AI in various application scenarios, such as the urban happiness index, carbon–neutral intelligent design, and the integration of virtual reality and real-world construction, promoting the development of cities toward smarter and greener futures.
APA, Harvard, Vancouver, ISO, and other styles
7

Mehta, Sheetal Navin, Alexander Roth, Clara Munteanu, and Swati Chandna. "AI-Based Pronunciation Assessment and Grammatical Error Correction with Feedback for the German Language." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93415-5_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 application that would enable the users create demos of robotic cells in VR-based environments, similar to the real ones. In this paper, the background of the architecture is introduced, then a description of the architecture is provided. In the use case, an AI-based application is demonstrated as example, while in the end, results and next steps are enclosed in the conclusions.
APA, Harvard, Vancouver, ISO, and other styles
9

Awan, Wardah Naeem, and Iflaah Salman. "Analyzing the Impact of Constant Feedback on Hybrid Agile Team Performance: Preliminary Results." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-72781-8_6.

Full text
Abstract:
AbstractThis study investigates the impact of constant feedback in enhancing hybrid scrum team performance from a case study conducted with 24 undergraduate students organized in three teams. This research uses self-perceived performance surveys to identify factors that affect team performance. The objective is to enhance team performance by providing timely feedback for reflection and improvement based on identified challenges. Preliminary findings revealed that constant feedback, facilitated by self-perceived performance surveys after each sprint cycle, enables teams to address identified challenges and enhance performance progressively. This highlights the significance of timely feedback in enhancing team performance and productivity. Future work involves leveraging AI tools to analyze communication data collected throughout the study to understand well-being factors and their influence on a team’s performance and productivity.
APA, Harvard, Vancouver, ISO, and other styles
10

Lindner, Annabel, Marc Berges, Mathias Rösch, and Florian Franke. "Implementing a Portable Learning Lab on Artificial Intelligence: It’s AI in a Box!" In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44900-0_3.

Full text
Abstract:
AbstractThis paper describes the conception, design, and first evaluation attempts of a learning lab on artificial intelligence (AI). The learning lab, which consists of 25 learning activities, aims to teach the central concepts of AI and its applications in everyday life, industry, and research. To design the learning arrangements, major concepts of AI were selected based on the literature and made accessible to the students through playful experiments. In addition, research- and industry-related activities were created in cooperation with experts. In the research-led development process, prototypes of the learning activities were tested with students and improved based on their feedback. An evaluation concept was created and used to assess the final activities.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "AI-based feedback"

1

Chen, Jingchun. "AI-Based Feedback Model in Supporting Mobile-Assisted Language Learning Environment." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796990.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Félix, Esther, Elaine De Oliveira, Ilmara Ramos, et al. "Designing Actionable and Interpretable Analytics Indicators for Improving Feedback in AI-Based Systems." In 17th International Conference on Computer Supported Education. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013294300003932.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Lai, Pauli, Ivan Lau, and Richard Pang. "Exploring the Efficacy of Rubric-Based AI Feedback in Enhancing Student Writing Outcomes." In 2024 6th International Workshop on Artificial Intelligence and Education (WAIE). IEEE, 2024. https://doi.org/10.1109/waie63876.2024.00047.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

M, Kaushik, Mahita Kandala, Vignesh G S, Vithyatharshana N, and Suja Palaniswamy. "AI-Based Posture Correction, Real-Time Exercise Tracking and Feedback using Pose Estimation Technique." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10932054.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Becerra, Alvaro, and Ruth Cobos. "Enhancing the Professional Development of Engineering Students through an AI-Based Collaborative Feedback System." In 2025 IEEE Global Engineering Education Conference (EDUCON). IEEE, 2025. https://doi.org/10.1109/educon62633.2025.11016499.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ilukpitiya, I. M. D. J. R. B., H. M. R. B. Herath, R. H. M. S. A. Rajakaruna, M. H. S. M. Herath, Koliya Pulasinghe, and Jenny Krishara. "AI-Driven Personalized Fitness Coaching with Body Type-Based Workout and Nutrition Plans and Real-Time Exercise Feedback." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928121.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Rago, Antonio, and Maria Vanina Martinez. "Advancing Interactive Explainable AI via Belief Change Theory." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/87.

Full text
Abstract:
As AI models become ever more complex and intertwined in humans’ daily lives, greater levels of interactivity of explainable AI (XAI) methods are needed. In this paper, we propose the use of belief change theory as a formal foundation for operators that model the incorporation of new information, i.e. user feedback in interactive XAI, to logical representations of data-driven classifiers. We argue that this type of formalisation provides a framework and a methodology to develop interactive explanations in a principled manner, providing warranted behaviour and favouring transparency and accountability of such interactions. Concretely, we first define a novel, logic-based formalism to represent explanatory information shared between humans and machines. We then consider real world scenarios for interactive XAI, with different prioritisations of new and existing knowledge, where our formalism may be instantiated. Finally, we analyse a core set of belief change postulates, discussing their suitability for our real world settings and pointing to particular challenges that may require the relaxation or reinterpretation of some of the theoretical assumptions underlying existing operators.
APA, Harvard, Vancouver, ISO, and other styles
8

Chenoll, Antonio. "THE EFFECTIVENESS, RECEPTION AND ETHICAL PERSPECTIVE OF GENERATIVE AI-BASED FEEDBACK IN THE TEACHING AND LEARNING PROCESS OF NON-NATIVE LANGUAGES." In 19th International Technology, Education and Development Conference. IATED, 2025. https://doi.org/10.21125/inted.2025.1179.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Lim, May. "WIP: Just-in-Time AI Assisted Formative Feedback for Written, Oral, Team-Based Assessment Tasks: What Worked, What Didn't and Why." In 2024 IEEE Frontiers in Education Conference (FIE). IEEE, 2024. https://doi.org/10.1109/fie61694.2024.10893330.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Trajkova, Milka. "Designing AI-Based Feedback for Ballet Learning." In CHI '20: CHI Conference on Human Factors in Computing Systems. ACM, 2020. http://dx.doi.org/10.1145/3334480.3375036.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "AI-based feedback"

1

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.

Full text
Abstract:
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 time generally was not explained by teachers’ (n=35) experience or self-reported instructional approaches. We also find that students’ growth increased significantly as their baseline score decreased (i.e., a student with the lowest possible baseline grew more than a student with a medium baseline). Lastly, based on an in-person convening of 16 Topeka teachers, we compared their scores and feedback to AI-generated scores and feedback on the same essays, finding that generally the AI tool was more generous, with differences likely driven by teachers’ ability to understand the whole essay’s success better than the AI tool.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 themselves and interacting with students in numerous ways. Others delegated aspects of assessment and learning to AI as a “grading assistant” to save time and increase efficiency, interacting with students with a slight emphasis on score attainment over skill development. Another group turned instruction over to the AI tool as if it were a “substitute teacher,” interacting minimally with students and placing themselves on the instructional periphery. We describe each approach in detail and discuss implications for teaching practices, teachers’ roles, the profession, and students’ experiences and opportunities.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'AI-based feedback' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography