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Journal articles on the topic 'AI grading'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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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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Mothoagae, Tshepo, and Nazeer Joseph. "The Design of a Bayesian Network Model for Increasing the Number of Graded Tourism Establishments." December 2020, no. 9(5) (December 31, 2020): 793–809. http://dx.doi.org/10.46222/ajhtl.19770720-52.

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Research has been conducted on the grading of tourism establishments but little research has been conducted on the implementation of Artificial Intelligence (AI) to increase the number of graded tourism establishments. The objective of this study was to identify variables influencing tourism grading and to use them to construct a Bayesian Model for increasing the number of tourism establishments. Data was collected using an online survey questionnaire developed using the Survey Monkey tool. A total of 87 responses were received from 60 non-graded and 27 graded tourism establishments. The resul

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Lin, Senlin, Yingyan Ma, Yi Xu, et al. "Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data." JMIR Public Health and Surveillance 9 (February 23, 2023): e41624. http://dx.doi.org/10.2196/41624.

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Background Community-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)–based and manual grading–based telemedicine screening is inadequate for policy making. Objective The aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China. Methods We conducted cost-effectiveness and cost-utility analyses by using decis

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Gu, Ziyue. "Research on the Accuracy of Machine Learning-Based AI Grading Systems in Handling High School Math Function Problems: A Comparative Study of MathGPTPro and Zuoyebang." Lecture Notes in Education Psychology and Public Media 74, no. 1 (2024): 158–65. https://doi.org/10.54254/2753-7048/2024.bo17887.

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As artificial intelligence (AI) technology becomes more prevalent in education, AI automatic grading systems have emerged as essential tools for enhancing homework grading efficiency and alleviating teachers' workloads. The two leading platforms, Zuoyebang and MathGPTPro, are widely utilized in mathematics education. This study employs an experimental research method to compare the performance of these platforms in automatically grading function solution problems. The sample consists of 30 questions from the Function Solution Problems section of China's National College Entrance Examination. T

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Wan, Zhijiang, Jiachen Wan, Wangxinjun Cheng, et al. "A Wireless Sensor System for Diabetic Retinopathy Grading Using MobileViT-Plus and ResNet-Based Hybrid Deep Learning Framework." Applied Sciences 13, no. 11 (2023): 6569. http://dx.doi.org/10.3390/app13116569.

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Traditional fundus image-based diabetic retinopathy (DR) grading depends on the examiner’s experience, requiring manual annotations on the fundus image and also being time-consuming. Wireless sensor networks (WSNs) combined with artificial intelligence (AI) technology can provide automatic decision-making for DR grading application. However, the diagnostic accuracy of the AI model is one of challenges that limited the effectiveness of the WSNs-aided DR grading application. Regarding this issue, we propose a WSN architecture and a parallel deep learning framework (HybridLG) for actualizing auto

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Mane, Pranjal Anil, and Dr Santosh Jagtap. "AI-Enhanced Educational Platform for Personalized Learning Paths, Automated Grading, and Real-Time Feedback." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39921.

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An AI-enhanced educational platform can significantly improve learning outcomes by tailoring instruction to individual needs. Studies indicate that personalized learning can boost student performance by up to 30% and enhance engagement by over 60% [20] . Automated grading can reduce instructors' grading time by nearly 40%, allowing more focus on student interaction [21] while real-time feedback has been shown to improve retention rates by approximately 50%[22], making education more adaptive and effective. This research presents an AI-enhanced educational platform designed to offerpersonalized

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Zhang, Jiali. "AI Agents in Education: Four Trends and a Practical Workflow." Proceedings of the AAAI Symposium Series 5, no. 1 (2025): 131–32. https://doi.org/10.1609/aaaiss.v5i1.35578.

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AI agents based on large language models (LLMs) are transforming education by offering personalized tutoring, faster grading, innovative content generation, and streamlined administrative tasks. This paper explores four major trends of AI agents in the education industry: tutoring and mentoring, automated grading, content creation, and administrative automation. This study also introduces a common AI agent prototype, focusing on customization through fine-tuning and retrieval-augmented generation (RAG), and highlights the role of external tools in enhancing versatility. By reviewing current ap

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Ragolane, Mahlatse, and Shahiem Patel. "Transforming Educ-AI-tion in South Africa: Can AI-Driven Grading Transform the Future of Higher Education?" Journal of Education and Teaching Methods 3, no. 1 (2024): 26–51. http://dx.doi.org/10.58425/jetm.v3i1.267.

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Aim: The objective of this paper was to explore students' attitudes and perceptions towards AI-driven grading systems in South African higher education, to ascertain whether AI grading can transform the future of higher education in South Africa. Methods: The study employed a qualitative methodology with an interpretive approach to gather the viewpoints, perceptions and experiences of higher education students in South Africa regarding AI grading of assessments. The sample consisted of thirteen (n=13) participants, including six (n=6) males and seven (n=7) females, ranging in age from 19 to 24

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Ms.M Kowsalya, S Gowtham, S Guhan, and S Logeshwar. "Precision: Paper Correction System Using AI." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 570–76. https://doi.org/10.47392/irjaeh.2025.0080.

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The education system has experienced a significant transformation due to technological advancement. Technology has made teaching much more interesting and informative through projectors, online tutorials, teaching videos, and animations. In classrooms, technology is now widely used in order to enhance learning experiences for students. The evaluation, however, tends to be traditional as exam papers are manually graded with a heavy reliance on the teacher's judgment. Replacing the manual way of grading with machine learning could very well tackle the inconvenience and errors human handling brin

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Ashish, Patil. "Significance of Artificial Intelligence in Carrier Performance Grading, Thus Improving Supply Chain Delivery Efficiency." International Journal of Leading Research Publication 6, no. 3 (2025): 1–9. https://doi.org/10.5281/zenodo.15109669.

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Carrier performance grading is critical for enhancing supply chain management efficiency, especially in the rapidly evolving e-commerce logistics sector. Traditional grading methods are mainly reliant on retrospective historical data, have limitations like insufficient predictive capabilities, delayed adaptability, and inefficiencies resulting from manual processes This paper examines predictive analytics and artificial intelligence (AI) as solutions to these challenges, focusing on supply chain optimization with an emphasis on carrier performance grading. The research shows an in-depth compar

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Pan, Xiaoi, Abdul Jabbar Khalid, Jose Coelho-Lima, et al. "The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma." Nature Cancer 5, no. 2 (2024): 347–63. https://doi.org/10.1038/s43018-023-00694-w.

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Abstract The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas a

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Bhogapurapu, Anusha, Das Pranati, and Hanumanthu Seshagiri. "AI-Enhanced Learning Approaches in English Language Teaching: A Review of Opportunities and Challenges." Literary Enigma 1, no. 2 (2025): 111–18. https://doi.org/10.5281/zenodo.15359934.

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Abstract   Artificial Intelligence (AI) has emerged as a transformative force in English Language Teaching (ELT), offering personalized and adaptive learning experiences. Research indicates that AI-driven platforms enhance individualized instruction by tailoring content to learners' proficiency levels and styles. AI chatbots provide interactive conversational practice with real-time feedback, while AI-powered assessment tools streamline evaluation processes, reducing teacher workload and ensuring objective grading. However, studies also highlight significant challenges, including AI's lim

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Soni Maitrik Chandrakant. "AI-powered teaching assistants: Enhancing educator efficiency with NLP-based automated feedback systems." International Journal of Science and Research Archive 14, no. 3 (2025): 009–18. https://doi.org/10.30574/ijsra.2025.14.3.0603.

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Further demands in education grading and feedback delivery have led to the development of AI teaching assistants using Natural Language Processing (NLP) systems. Automated systems support grading efficiency through analysis of student work, which provides instant, consistent, and useful feedback to students. AI evaluation software supports educators to manage workload more effectively while preserving high assessment standards. Evaluating written responses with NLP tools enables teachers to examine grammatical elements, structural organization, and content organization to achieve better studen

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Luca Bianchi and Sofia Rossi. "The Role of Artificial Intelligence in Everyday Applications in Education." International Journal of Emerging Trends in Information Technology (IJEIT) 1, no. 1 (2025): 13–27. https://doi.org/10.64056/4ppeqb91.

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Artificial Intelligence (AI) is increasingly transforming education by enabling novel teaching and learning methods. This study examines recent developments in AI applications within everyday educational settings, assessing their impact on instruction, student learning, and administrative processes. A systematic literature review of scholarly sources (2019–2024) was conducted following PRISMA guidelines. The review identified key AI-driven innovations in personalized learning, intelligent tutoring, automated assessment, and virtual assistance. Many studies report that AI tools (e.g., adaptive

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Egevad, Lars, Daniela Swanberg, Brett Delahunt, et al. "Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading." Virchows Archiv 477, no. 6 (2020): 777–86. http://dx.doi.org/10.1007/s00428-020-02858-w.

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AbstractThe International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68–0.84) and

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Wang, Jinsheng. "The biomechanics-inspired application of AI technology in English essay correction." Molecular & Cellular Biomechanics 22, no. 4 (2025): 1525. https://doi.org/10.62617/mcb1525.

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This paper explores the application of AI technology in the field of English essay grading, inspired by biomechanics. Biomechanics, which studies the mechanical aspects of biological systems, offers unique insights that can be analogously applied to the grading of English compositions. Just as biomechanics analyzes the complex structures and functions of biological entities by understanding the relationships between different components, we focus on natural language processing (NLP) and machine learning algorithms, with the primary objective is to analyze how these advanced technologies, inspi

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Ghosh, Arunava, and Tuhin Sengupta. "Intello Labs: Non-Destructive Digital Commodity Grading." Emerging Economies Cases Journal 3, no. 1 (2021): 51–57. http://dx.doi.org/10.1177/25166042211031372.

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This case illustrates Intello Labs, a leading agri-tech company, operating out of Gurgaon. As an agri-tech company, Intello Labs is trying to create an artificial intelligence (AI)-based solution model for its clients. The case dives deep into the issues of degrading farm productivity being faced by Kerala Cardamom Processing and Marketing Company (KCPMC), a client of Intello Labs. The case stands out as a means for understanding the application of the AI-based solutions being offered by Intello Labs to solve the degrading farm productivity issue of KCPMC. It addresses the concerns of the curr

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Dusi, Prerna, and Pooja Sharma. "Development of Ai-Based Crop Quality Grading Systems using Image Recognition." SHS Web of Conferences 216 (2025): 01012. https://doi.org/10.1051/shsconf/202521601012.

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Crops' quality assessment in the current agricultural environment is still a labour intensive process relying much on human judgment. Often traditional crop grading methods are inconsistent of errors and tend to be inefficient and suboptimal grading results which cause costs. This research makes a contribution to the solution of these challenges by proposing a novel and automated crop quality grading system based on the use of advanced image recognition techniques. It also integrate Convolutional Neural Networks (CNN), Transfer Learning, Support Vector Machines (SVM) and Random Forest algorith

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Abdulrhman Fadul, Dr Khalid Adam. "Investigating the Strategies to Mitigate the Influence of AI Tools on Blackboard Evaluations." International Journal of Advanced Multidisciplinary Research and Studies 5, no. 2 (2025): 729–40. https://doi.org/10.62225/2583049x.2025.5.2.3891.

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This study investigates strategies to mitigate the influence of AI tools on Blackboard evaluations. The hypothesis suggests that extensive use of AI in evaluations raises concerns about academic dishonesty and assessment reliability. Students may exploit AI to create answers, undermining the validity of their work. Furthermore, depending too much on AI for feedback and grading can generate biases and inaccuracies, reducing assessment reliability. The aim of the study is to provide practical ways for addressing these difficulties and ensuring that AI technologies improve, rather than subtract f

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Boreddy, Sandeep Reddy. "AI Driven Handwritten Assignment Analysis and Evaluation." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 6810–15. https://doi.org/10.22214/ijraset.2025.70012.

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Manual evaluation of handwritten assignments is a time-consuming and labor-intensive process that burdens faculty members in educational institutions. This paper presents a novel AI-driven system designed to automate the evaluation of handwritten assignments using Optical Character Recognition (OCR) and Natural Language Processing (NLP). The system ensures accuracy, consistency, and efficiency in grading while reducing administrative workload. Unique assignment questions are generated from a robust question bank and distributed via institutional email. Students submit scanned handwritten respo

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Khalid, Usman, Jasmin Gurung, Mladen Doykov, et al. "Artificial Intelligence Algorithms and Their Current Role in the Identification and Comparison of Gleason Patterns in Prostate Cancer Histopathology: A Comprehensive Review." Diagnostics 14, no. 19 (2024): 2127. http://dx.doi.org/10.3390/diagnostics14192127.

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The development of the Gleason grading system has proven to be an irreplaceable tool in prostate cancer diagnostics within urology. Despite the advancements and developments in diagnostics, there remains a discrepancy in the grading process among even the most experienced pathologists. AI algorithms have demonstrated potential in detecting cancer and assigning Gleason grades, offering a solution to the issue of significant variability among pathologists’ evaluations. Our paper explores the evolving role of AI in prostate cancer histopathology, with a key focus on outcomes and the reliability o

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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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Harriet Akudo, Agbarakwe,, and Chibueze, Ozioma Ogbonna. "Leveraging Artificial Intelligence for Enhanced Assessment and Feedback Mechanisms in Nigeria Higher Education System." International Journal of Research and Innovation in Social Science VIII, no. IX (2024): 142–51. http://dx.doi.org/10.47772/ijriss.2024.809012.

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This paper explores the inherent potentials of Artificial Intelligence (AI) in enhancing assessment and feedback mechanisms within Nigeria’s higher education system. The traditional assessment methods in Nigerian institutions often suffer from issues like inconsistent grading, delayed feedback, and significant administrative burdens on educators, which impede timely student interventions. AI technologies, with their capabilities in automation, data processing, and pattern recognition, offer solutions by enabling efficient, equitable, and personalized assessment systems. Automated grading, inte

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Nadir Masud Malik, Muhammad Haseeb, and Huzafa Ali. "Role of Artificial Intelligence in Grading and Prognosis of Prostate Cancer." Journal of Health and Rehabilitation Research 4, no. 2 (2024): 1–2. http://dx.doi.org/10.61919/jhrr.v4i2.891.

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Artificial Intelligence (AI) and Machine Learning (ML) are increasingly influencing the medical field, particularly in the diagnosis and prognosis of prostate cancer. AI technologies facilitate complex tasks in identifying and characterizing prostate cancer through image-based analyses, including histopathology and MRI. These advancements are enhancing evaluation methods and improving patient outcomes by incorporating additional data such as demographic factors and experimental markers into risk prediction models, increasing the accuracy of prostate cancer prognosis from 60% to 80%. ML, a subs

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Karti, Omer, and Ali Osman Saatci. "Grading scales for vitreous haze." Medical hypothesis, discovery & innovation in optometry 5, no. 3 (2025): 127–35. https://doi.org/10.51329/mehdioptometry207.

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Background: Grading scales for vitreous haze are crucial for the diagnosis, monitoring, and management of uveitis. The presence of inflammatory cells within the vitreous cavity is widely recognized as a key indicator of disease activity and severity, offering valuable insights into the underlying inflammatory processes. This mini-review aims to explore the evolution of vitreous haze grading scales systematically, emphasizing conventional grading methods, advances in imaging technologies, and the integration of artificial intelligence (AI) into the grading process. Methods: The PubMed/MEDLINE d

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PRATAP SINGH, DIVYANSH. "Impact of Artificial Intelligence on the Academic Environment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44260.

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Integrating Artificial Intelligence (AI) in education has significantly transformed teaching methodologies, assessment systems, and student engagement. This study evaluates the impact of AIdriven personalized learning and compares AI-assisted grading systems with traditional methods. The research examines the relationship between AI adoption and student performance using a dataset of 115 respondents and applying statistical techniques such as correlation analysis, chi-square tests, and parabolic distribution modelling. Findings reveal that while AI contributes to improved learning outcomes, it

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Daxenberger, Fabia, Maximilian Deußing, Quirine Eijkenboom, et al. "Innovation in Actinic Keratosis Assessment: Artificial Intelligence-Based Approach to LC-OCT PRO Score Evaluation." Cancers 15, no. 18 (2023): 4457. http://dx.doi.org/10.3390/cancers15184457.

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Actinic keratosis (AK) is a common skin cancer in situ that can progress to invasive SCC. Line-field confocal optical coherence tomography (LC-OCT) has emerged as a non-invasive imaging technique that can aid in diagnosis. Recently, machine-learning algorithms have been developed that can automatically assess the PRO score of AKs based on the dermo-epidermal junction’s (DEJ’s) protrusion on LC-OCT images. A dataset of 19.898 LC-OCT images from 80 histologically confirmed AK lesions was used to test the performance of a previous validated artificial intelligence (AI)-based LC-OCT assessment alg

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Krishna Kashyap Yakkala. "AI-powered assessment tools for E-learning: Enhancing feedback and grading systems." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 792–802. http://dx.doi.org/10.30574/wjaets.2024.13.1.0497.

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Recently, there has been increased incorporation of artificial intelligence in the education system, which has impacted the practice and use of assessment in learning. To this end, the following is a paper discussing the role of AI in grading, instant feedback, and personalized adaptive testing. The paper also covers the ability of AI to minimize bias and introduce fairness while grading, as well as the ability that comes with the mass LMS platforms. Moreover, this paper discusses the issues and guidelines regarding the ethical use of AI in education: data protection, the problem of automated

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Sokač, Mateo, Mario Fabijanić, Igor Mekterović, and Leo Mršić. "Automated Grading Through Contrastive Learning: A Gradient Analysis and Feature Ablation Approach." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 41. https://doi.org/10.3390/make7020041.

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As programming education becomes increasingly complex, grading student code has become a challenging task. Traditional methods, such as dynamic and static analysis, offer foundational approaches but often fail to provide granular insights, leading to inconsistencies in grading and feedback. This study addresses the limitations of these methods by integrating contrastive learning with explainable AI techniques to assess SQL code submissions. We employed contrastive learning to differentiate between student and correct SQL solutions, projecting them into a high-dimensional latent space, and used

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Karabeg, Mia, Goran Petrovski, Katrine Holen, et al. "Comparison of Validity and Reliability of Manual Consensus Grading vs. Automated AI Grading for Diabetic Retinopathy Screening in Oslo, Norway: A Cross-Sectional Pilot Study." Journal of Clinical Medicine 14, no. 13 (2025): 4810. https://doi.org/10.3390/jcm14134810.

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Background: Diabetic retinopathy (DR) is a leading cause of visual impairment worldwide. Manual grading of fundus images is the gold standard in DR screening, although it is time-consuming. Artificial intelligence (AI)-based algorithms offer a faster alternative, though concerns remain about their diagnostic reliability. Methods: A cross-sectional pilot study among patients (≥18 years) with diabetes was established for DR and diabetic macular edema (DME) screening at the Oslo University Hospital (OUH), Department of Ophthalmology, and the Norwegian Association of the Blind and Partially Sighte

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Sedova, Aleksandra, Dorottya Hajdu, Felix Datlinger, et al. "Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images." Eye 36, no. 3 (2022): 510–16. http://dx.doi.org/10.1038/s41433-021-01912-4.

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Abstract Introduction Comparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an FDA-approved screening system and human retina specialists’ gradings from ultra-widefield (UWF) colour images. Methods Asymptomatic diabetics without a previous diagnosis of DR were included in this prospective observational pilot study. Patients were imaged with autonomous AI (IDx-DR, Digital Diagnostics). For each eye, two 45° colour fundus images were analysed by a secure server-based AI algorithm. UWF colour fundus imaging was performed using Optomap

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Skevas, Christos, Max Levering, Jonne Engelberts, Mark van Grinsven, and Toam Katz. "Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos—a prospective analysis of the RetCAD system." International Journal of Ophthalmology 15, no. 12 (2022): 1985–93. http://dx.doi.org/10.18240/ijo.2022.12.14.

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AIM: To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos. METHODS: This prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel graded by the RetCAD AI software and by an expert reference examiner for image quality, and staging of AMD and DR. Adjudication was provided by a second expert examiner in case of disagreement between the

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Almasre, Miada. "Development and Evaluation of a Custom GPT for the Assessment of Students’ Designs in a Typography Course." Education Sciences 14, no. 2 (2024): 148. http://dx.doi.org/10.3390/educsci14020148.

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The recent advancements in the fields of AI technology, generative AI, and Large Language Models (LLMs) have increased the potential of the deployment of such tools in educational environments, especially in contexts where student assessment fairness, quality, and automation are a priority. This study introduces an AI-enhanced evaluation tool that utilizes OpenAI’s GPT-4 and the recently released custom GPT feature to evaluate the typography designs of 25 students enrolled in the Visual Media diploma offered by King Abdulaziz University. A mixed methods approach is adopted to evaluate the perf

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Alshehri, Asmaa, Salha Hassan Qahl, and Najlaa Alharbi. "Utilizing Artificial Intelligence to Assess ESL Students' Narratives: A Comparative Analysis." Journal of Posthumanism 5, no. 6 (2025): 1533–49. https://doi.org/10.63332/joph.v5i6.2224.

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This study investigates the effectiveness, reliability, and potential biases of AI-based assessment tools in evaluating narrative essays written by undergraduate ESL students at a Saudi university. A total of 30 essays were assessed using a detailed rubric covering five writing components: ideas and content, organization, vocabulary, voice and style, and mechanics and formatting. The essays were graded by human evaluators and five AI tools—ChatGPT, Gemini, Claude, Justdone, and Chatsonic. A quantitative comparative research design was employed, and statistical analyses, including one-way ANOVA

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Köteles, Maria Magdalena, Alon Vigdorovits, Darshan Kumar, et al. "Comparative Evaluation of Breast Ductal Carcinoma Grading: A Deep-Learning Model and General Pathologists’ Assessment Approach." Diagnostics 13, no. 14 (2023): 2326. http://dx.doi.org/10.3390/diagnostics13142326.

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Breast cancer is the most prevalent neoplasia among women, with early and accurate diagnosis critical for effective treatment. In clinical practice, however, the subjective nature of histological grading of infiltrating ductal adenocarcinoma of the breast (DAC-NOS) often leads to inconsistencies among pathologists, posing a significant challenge to achieving optimal patient outcomes. Our study aimed to address this reproducibility problem by leveraging artificial intelligence (AI). We trained a deep-learning model using a convolutional neural network-based algorithm (CNN-bA) on 100 whole slide

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Hrishikesh, Rajulu, Reddy Charan, Pendem Naveen, TarizAtique, Shwetha Buchanalli Dr., and Kumar Depuru Bharani. "Embryo Grading Redefined: AI Innovations Shaping the Future of Fertility." Embryo Grading Redefined: AI Innovations Shaping the Future of Fertility 8, no. 11 (2023): 7. https://doi.org/10.5281/zenodo.10353687.

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Fertility treatments, particularly in the context of in vitro fertilization (IVF), have seen significant advancements in recent years, revolutionizing the prospects for couples facing infertility. The quality of the embryo is a critical factor influencing the success of these treatments. Traditional methods for embryo assessment have limitations in accuracy and efficiency, prompting the need for innovative techniques. This research study explores the application of deep learning models to enhance embryo quality assessment in the field of reproductive medicine. The study involves the collection

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A., Okorie E., Akande O. M., and Yateghtegh S. K. "Exam Script Verification System Using Artificial Intelligence." International Journal of Research 10, no. 8 (2023): 120–36. https://doi.org/10.5281/zenodo.8308147.

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<strong>The process of manually verifying exam scripts is time-consuming, error-prone, and often lacks consistency, particularly in Nigerian educational institutions. This study proposes an innovative exam script verification system that utilizes artificial intelligence (AI) techniques to automate and improve the efficiency of the verification process. The research aims to enhance the accuracy and reliability of grading by leveraging AI algorithms for evaluating exam scripts. The system employs machine learning models to analyze and assess various aspects of the scripts, including content, str

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Kushwaha, Saurabh, Rajat Chaudhary, and Uma Devi. "Artificial Intelligence: A Review of Objective Grading and Quantification of Posterior Capsular Opacification." Delhi Journal of Ophthalmology 33, no. 1 (2023): 9–17. https://doi.org/10.4103/dljo.dljo_7_23.

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In ophthalmology, artificial intelligence (AI)-assisted system is being widely used for screening and diagnosis of posterior segment diseases such as diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, and glaucoma. However, anterior segment disease’s diagnosis is largely dependent on clinical examination and hence is more observers biased and prone to human errors. Cataract is the leading cause of blindness worldwide and cataract surgery is the most common ophthalmic surgical procedure performed in the world. With improvised surgical techniques the incidence of

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Hendrik, Manopo Jouke, Umar Paputungan, and Wapsiaty Utiah. "KAJIAN APLIKASI TEKNIK INSEMINASI BUATAN DALAM PENINGKATAN GENETIK BOBOT BADAN TERNAK SAPI PERANAKAN ONGOLE DI SULAWESI UTARA." ZOOTEC 40, no. 2 (2020): 700. http://dx.doi.org/10.35792/zot.40.2.2020.30103.

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THE STUDY OF ARTIFICIAL INSEMINATION APPLICATION ON GENETIC IMPROVEMENT OF LIVE WEIGHT IN ONGOLE GRADE CATTLE AT NORTH SULAWESI PROVINCE. This research was conducted to evaluate the effective application of artificial insemination compared with natural breeding on genetic improvement by grading up system involving semen sourced from Ongole bull for the Indonesian-grade cattle at rural areas in Minahasa regency of North Sulawesi province, Indonesia. Data on animal live weight were collected from all cows (n= 404) kept by the traditional household farmers. Data of the cows were corrected by adju

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Amoo, Tesleem Akinyemi. "Ethical Consideration in Implementing AI-based Tutoring Systems as Educational Technology Tool in Education: Balancing Efficiency with Privacy and Equity in the Teaching of Students." FINGER : Jurnal Ilmiah Teknologi Pendidikan 4, no. 1 (2025): 34–41. https://doi.org/10.58723/finger.v4i1.365.

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This article explores the major technological advancements in education and the ethical issues they raise. In this research, we use the systematic literature mapping method. The study follows the research methodology outlined by Kabudi, Pappas, and Olsen, with guidance from Petersen, Vakkalanka, and Kuzniarz. The methodology applied in both studies is as follows: (i) search and selection, (ii) data extraction, (iii) classification and analysis, and (iv) evaluation of validity. The PRISMA approach, or Preferred Reporting Items for Systematic Reviews and Meta-Analyses, was used as a framework fo

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Ma, Sihan. "Comparison of Different AIED Models and Evaluation Methods." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 401–8. http://dx.doi.org/10.54097/yh47p531.

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In the educational field, although machine which attempts to learn AI is still in their early stages, the approach has yet to show remarkable results when facing complex challenges without obvious cut-off points, such as grading students’ papers or exploring enormous and complicated data collections. AI can also be used to create virtual learning environments, intelligent testing systems, and automated grading systems. AI in educational fields refers to the application of AI technology to enhance and support the studying processes, such as tracking students’ behavior and constructing models th

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