Relevant bibliographies by topics / AI-based feedback

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Academic literature on the topic 'AI-based feedback'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AI-based feedback"

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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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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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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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K, Dr Vanishree, and Sowmya S. "AI-Based Framework for Career Advancement." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51583.

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The advancement of artificial intelligence (AI) has opened new possibilities in education and career development. With rising demand for skilled professionals in the digital era, there is a growing need for scalable platforms that provide real-time feedback and personalized career guidance. Existing platforms lack voice-based interaction, personalized guidance, and comprehensive feedback mechanisms. This project addresses these gaps by developing an integrated AI-Based Framework for Career Advancement that combines voice-enabled mock interviews, personalized career planning, resume generation,
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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.

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

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

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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-based feedback"

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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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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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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.

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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-tim
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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.

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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.

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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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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.

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Karagiannis, Panagiotis, Panagiotis Angelakis, and Sotiris Makris. "On an AI-Based System Architecture to Generate Robotic Cells in VR Environments." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_21.

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Abstract This paper focuses on the design and implementation of an AI-based architecture diagram. Each module of this diagram contributes to the generation and programming of different components, namely robot, fixtures, grippers, fences etc. that are necessary to create a robotic cell in virtual environments. Additionally, the application includes modules that perform self-correcting suggestions, checking for any anomalies, such as overlapping parts, in the generated scene, providing corrective feedback to the user. The aim of the architecture is to help with the creation of an AI-based appli
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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.

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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 ch
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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.

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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, pro
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Conference papers on the topic "AI-based feedback"

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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.

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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.

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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.

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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.

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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.

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Wang, Yi, Zichen Luo, Dongpo Guo, Zhijin Zhou, and Fred Paas. "Impacts of a progressive feedback prompt-based text-generative AI approach on Chinese poetry learning." In 2025 7th International Conference on Computer Science and Technologies in Education (CSTE). IEEE, 2025. https://doi.org/10.1109/cste64638.2025.11092104.

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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.

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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.

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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 account
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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.

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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.

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Reports on the topic "AI-based feedback"

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

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

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This study investigates how teachers understand and position AI tools in middle school writing instruction, drawing on 27 teacher interviews collected during a study called Project Topeka that used an interactive argumentative writing platform with AI-generated scores and feedback. Based on the interviews, we generate an initial theoretical framework of how teachers position AI tools — and therefore themselves — in their teaching. We found that some teachers leveraged AI as a “teaching partner” that provided insights to help enhance teaching and learning while remaining central to instruction
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