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Relevant bibliographies by topics / AI grading

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Academic literature on the topic 'AI grading'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "AI grading"

1

Brown, Sherrill J., David D. Allen, and Staci Hemmer. "AI Grading versus Traditional Grading for Pharmacy Writing Assignments." American Journal of Pharmaceutical Education 88, no. 9 (2024): 100866. http://dx.doi.org/10.1016/j.ajpe.2024.100866.

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Cheng Liang, Dylan Yeo, Chhan Kai Ming, Kua Deng Yao, Foo Ming Yee, and Noraisyah Mohamed Shah. "AI-Assisted Grading on Harumanis Mango." Journal of New Explorations in Electrical Engineering 1, no. 1 (2025): 25–39. https://doi.org/10.22452/nece.vol1no1.3.

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In recent years, the increasing demand for fruits has presented significant challenges for fruit farmers and distributors, particularly in the realm of effective fruit management. A crucial aspect of this management is fruit grading, which is essential for assessing quality. Traditional manual grading methods, however, are prone to errors and inefficiencies, leading to inaccurate assessments of fruit maturity and quality. These inaccuracies cause substantial economic losses for distributors and hinder farmers' ability to deliver high-quality fruits to the market. This study focuses on the Haru

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KumarI, S. Karuna. "AI-Driven OCR-Based Script Grading." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2073–78. https://doi.org/10.22214/ijraset.2025.70646.

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Abstract: AI-Driven OCR-Based Script Grading System is a novel web-based solution designed to automate the evaluation of descriptive answers in educational assessments, addressing significant challenges associated with manual grading processes, such as inconsistent scoring and high time consumption. This system integrates cutting-edge technologies like Optical Character Recognition (OCR), Natural Language Processing (NLP), and advanced summarization techniques to streamline answer script evaluations effectively. EasyOCR is utilized to accurately extract textual content from scanned handwritten

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Mok, Ryan, Faraaz Akhtar, Louis Clare, et al. "Using large language models for grading in education: an applied test for physics." Physics Education 60, no. 3 (2025): 035006. https://doi.org/10.1088/1361-6552/adb92b.

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Abstract Grading assessments is time-consuming and prone to human bias. Students may experience delays in receiving feedback that may not be tailored to their expectations or needs. Harnessing AI in education can be effective for grading undergraduate physics problems, enhancing the efficiency of undergraduate-level physics learning and teaching, and helping students understand concepts with the help of a constantly available tutor. This report devises a simple empirical procedure to investigate and quantify how well large language model (LLM) based AI chatbots can grade solutions to undergrad

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Joshua Kyle Beltran, Dominee Kyle Ibarlin, Mark Ivan Mapa, and Edwin Romeroso Arboleda. "Exploring computer vision, machine learning, and robotics applications in banana grading: A review." International Journal of Science and Research Archive 11, no. 1 (2024): 1159–66. http://dx.doi.org/10.30574/ijsra.2024.11.1.0180.

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This paper presents a literature study on the applications of Artificial Intelligence (AI) in banana grading. The traditional approach of banana grading, primarily dependent on manual labor, is not only time-consuming but also receptive to subjective variations. The need for more accurate and efficient solutions has become increasingly necessary. With the help of AI technology, we can better address the evolving challenges and demands in automating the grading processes of banana-related agriculture. The study focuses on three sub-domains of AI: Computer Vision, Machine Learning, and Robotics.

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Alamäki, Ari, Umair Ali Khan, Janne Kauttonen, and Stephan Schlögl. "An Experiment of AI-Based Assessment: Perspectives of Learning Preferences, Benefits, Intention, Technology Affinity, and Trust." Education Sciences 14, no. 12 (2024): 1386. https://doi.org/10.3390/educsci14121386.

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The rising integration of AI-driven assessment in education holds promise, yet it is crucial to evaluate the correlation between trust in general AI tools, AI-based scoring systems, and future behavioral intention toward using these technologies. This study explores students’ perspectives on AI-assisted assessment in higher education. We constructed a comprehensive questionnaire supported by relevant studies. Several hypotheses grounded in the literature review were formulated. In an experimental setup, the students were tasked to read a designated chapter of a paper, answer an essay question

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Auffret, Nicole, Marie-Thérèse Leccia, Fabienne Ballanger, Jean Paul Claudel, Serge Dahan, and Brigitte Dréno. "Acne-induced Post-inflammatory Hyperpigmentation: From Grading to Treatment." Acta Dermato-Venereologica 105 (April 22, 2025): adv42925. https://doi.org/10.2340/actadv.v105.42925.

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Acne-induced post-inflammatory hyperpigmentation (AI-PIH) can occur without any visible clinical evidence of significant inflammation, even in patients with mild to moderate acne. Currently, visual assessment is the main criterion for evaluating the severity of PIH, including that of AI-PIH in daily clinical practice. Treatment indications are lacking. This work provides an easy-to-use AI-PIH severity grading tool for daily clinical practice as well as indications on how to prevent and treat AI-PIH using currently available treatment options. Five experts in acne provided a short overview conc

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Anjum, Prof Gulafsha, Prof Jaya Choubey, Shubhanshu Kushwaha, and Vandana Patkar. "AI in Education: Evaluating the Efficacy and Fairness of Automated Grading Systems." International Journal of Innovative Research in Science,Engineering and Technology 12, no. 06 (2023): 1–14. http://dx.doi.org/10.15680/ijirset.2023.1206161.

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The integration of Artificial Intelligence (AI) in educational settings has garnered significant attention, particularly in the realm of automated grading and feedback. Traditional grading methods are labor-intensive, timeconsuming, and prone to human bias, highlighting the need for AI-driven solutions to enhance grading efficiency, accuracy, and consistency. This study explores the application of machine learning algorithms and natural language processing techniques in developing automated grading systems. These systems can evaluate and score a wide range of student work, from multiple-choice

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Ragolane, Mahlatse, Shahiem Patel, and Pranisha Salikram. "AI Versus Human Graders: Assessing the Role of Large Language Models in Higher Education." Asian Journal of Education and Social Studies 50, no. 10 (2024): 244–63. http://dx.doi.org/10.9734/ajess/2024/v50i101616.

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While AI grading is seeing an increase in use and adoption, traditional educational practices are also forced to adapt and function together with AI, especially in assessment grading. In retrospect, human grading, on the other hand, has long been the cornerstone of educational assessment. Traditionally, educators have assessed student work based on established criteria, providing feedback intended to support learning and development. While human grading offers nuanced understanding and personalized feedback, it is also subject to limitations such as grading inconsistencies, biases, and signifi

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Greiner, Christian, Thomas Peisl, Felix Höpfl, and Olivia Beese. "Acceptance of AI in Semi-Structured Decision-Making Situations Applying the Four-Sides Model of Communication—An Empirical Analysis Focused on Higher Education." Education Sciences 13, no. 9 (2023): 865. http://dx.doi.org/10.3390/educsci13090865.

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This study investigates the impact of generative AI systems like ChatGPT on semi-structured decision-making, specifically in evaluating undergraduate dissertations. We propose using Davis’ technology acceptance model (TAM) and Schulz von Thun’s four-sides communication model to understand human–AI interaction and necessary adaptations for acceptance in dissertation grading. Utilizing an inductive research design, we conducted ten interviews with respondents having varying levels of AI and management expertise, employing four escalating-consequence scenarios mirroring higher education dissertat

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Books on the topic "AI grading"

1

Anderlini, Gianpaolo. I quindici gradini: Un commento ai Salmi 120-134. Giuntina, 2012.

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Dolfi, Anna, ed. Non finito. Opera interrotta e modernità. Firenze University Press, 2015. http://dx.doi.org/10.36253/978-88-6655-729-6.

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Non finito, opera interrotta… difficile trovare una definizione, circoscrivere il tema, distinguere il caso dall’intenzionalità. Certo pochi ‘generi’ e/o declinazioni hanno come il non finito bisogno di ciò che è esterno all’opera e che in qualche modo la completa, collocandola in posizione privilegiata per la sintonia con la nostra inquieta modernità. Non stupisce che in letteratura siano naturaliter ‘sospesi’ – oltre a ciò che è stato brutalmente interrotto – gli epistolari, i diari, le cronache della malattia e della sofferenza; né che l’incompiutezza accompagni gli scritti che rinviano a g

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

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

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

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

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

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Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip it

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Book chapters on the topic "AI grading"

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Schneider, Jan Georg, and Katharina A. Zweig. "Grade Prediction Is Not Grading." In KI-Kritik / AI Critique. transcript Verlag, 2023. http://dx.doi.org/10.14361/9783839466605-005.

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Aureo, Andriya Rose, N. B. Lemia, Parvin M. N. Nizma, Sebin Jose, and Shifin George. "AI-Based Grading of Handwritten Responses." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0228-5_25.

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Braude, Eric. "Value-Added Grading of AI-Assisted Papers." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44668-9_31.

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McGinness, Lachlan. "Overview of AI Grading of Physics Olympiad Exams." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-99261-2_55.

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Hall, Erin, Mohammed Seyam, and Daniel Dunlap. "Identifying Usability Challenges in AI-Based Essay Grading Tools." In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36336-8_104.

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Sil, Pritam, Parag Chaudhuri, and Bhaskaran Raman. "Can AI Assistance Aid in the Grading of Handwritten Answer Sheets?" In Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64312-5_35.

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Jackson, Stephen, and Niki Panteli. "A Multi-level Analysis of Mistrust/Trust Formation in Algorithmic Grading." In Responsible AI and Analytics for an Ethical and Inclusive Digitized Society. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85447-8_61.

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Dubey, Deepa, Kamal Pant, Aditya Tripathi, Priti Yadav, Gaurav Dubey, and Renu Thakur. "AI-driven early detection of cataract, its type and grading: A review." In Smart Computing and Communication for Sustainable Convergence. CRC Press, 2025. https://doi.org/10.1201/9781003637530-58.

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Rivas, Pablo, and Donald R. Schwartz. "Explainable AI for SQL Grading: A Practical Approach with Multi-task CNNs." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86623-4_5.

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Ghoneim, Mohamed, Radwa Hussein, and Nada Sharaf. "Towards Enhanced Manufacturing Precision: The Role of AI-Driven Quality Grading Systems." In Progress in IS. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85284-8_5.

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Conference papers on the topic "AI grading"

1

S, Aparna, Madhurema V J, Nandana Nair, Parvathi Mohan, and Rajeev Rajan. "AI-based Diabetic Retinopathy Grading Using Raspberry Pi." In 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2024. https://doi.org/10.1109/spices62143.2024.10779706.

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Prateek, Afraz Tanvir, and Brindha R. "AI-Powered Workout Analysis Application for Posture Feedback and Repitition Grading." In 2024 Second International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2024. http://dx.doi.org/10.1109/icici62254.2024.00027.

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Havare, Jayant, Varsha Apte, Kaushikraj Maharajan, et al. "Ai-Based Automated Grading of Source Code of Introductory Programming Assignments." In 2025 IEEE/ACM 33rd International Conference on Program Comprehension (ICPC). IEEE, 2025. https://doi.org/10.1109/icpc66645.2025.00025.

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Manu, Y. M., and M. E. Priyanka. "AI-Driven Grading of Mango Quality Using External Characteristics: A Machine Learning Approach." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10895220.

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Ghazal, Mohammed, Taimur Hassan, Jawad Yousaf, et al. "Diabetic Retinopathy Detection and Grading AI for Mobile and Hand-held Devices: A Readiness Survey." In 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2024. http://dx.doi.org/10.1109/ficloud62933.2024.00044.

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Dimari, Amit, Nidhi Tyagi, Mahesh Davanageri, Ravish Kukreti, Rajkumar Yadav, and Hema Dimari. "AI-Based Automated Grading Systems for open book examination system: Implications for Assessment in Higher Education." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616490.

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Li, Changyi, Jiang Long, Ning Guan, Yongqin Wang, Wenlong Liang, and Chao Li. "AI-Assisted College Students' English Writing Scoring with Hierarchical LSTM for Enhanced Contextual Understanding and Grading Accuracy." In 2024 7th International Conference on Education, Network and Information Technology (ICENIT). IEEE, 2024. https://doi.org/10.1109/icenit61951.2024.00039.

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Mazzone, Samuel B., Jack Forden, and Dennis Brylow. "Exploring the Potential of Locally Run Large Language (AI) Models for Automated Grading in Introductory Computer Science Courses." In 2024 IEEE Frontiers in Education Conference (FIE). IEEE, 2024. https://doi.org/10.1109/fie61694.2024.10892816.

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Holland, Anna, and Constantin Ciachir. "AN EXPLORATORY STUDY OF STUDENT PERSPECTIVES ON THE USE OF GENERATIVE AI FOR SUMMATIVE ASSESSMENT GRADING AND FEEDBACK IN HIGHER EDUCATION." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.2183.

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Pavithra, E., A. Mummoorthy, S. K. Manikandan, Yuvaraj.M, N. S. Gowri Ganesh, and M. Gayatri. "Vogt AI: A Deep Learning Framework for Early Detection and Severity Grading of Diabetic Macular Edema Through Retinal Imaging and Clinical Insights." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895626.

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Reports on the topic "AI grading"

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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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Doo, Johnny. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry. SAE International, 2024. https://doi.org/10.4271/epr2024028.

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<div class="section abstract"><div class="htmlview paragraph">Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation ind

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes

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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. no

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