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Exams spanning numerical and verbal formats, multiple assessment styles (project, test, and traditional), and both theoretical and practical courses were selected via criterion sampling. Bland-Altman and intraclass correlation (ICC) analyses were used to evaluate agreement across a broad range. Results show high proficiency in total score agreement, medium consistency for visually based exams, low consistency for video exams and traditional exams, and high consistency for test exams. Findings guide improvements for accurate, fair educational assessment tools.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/evaluating-the-evaluators-a-comparative-study-of-ai-and-teacher-assessments-in-higher-education/601532/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/evaluating-the-evaluators-a-comparative-study-of-ai-and-teacher-assessments-in-higher-education/601532.png","ImageObject",300,407,{"name":92,"@type":93},"anakgang17","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the study compare AI-based assessments with teacher evaluations?","Question",{"text":112,"@type":113},"It compares teacher evaluations and AI tool-based assessment systems on university exam scores using Bland-Altman and intraclass correlation coefficient (ICC) analyses across different exam types and assessment styles.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which exam formats showed the highest and lowest consistency between AI and teachers?",{"text":117,"@type":113},"The study reports high consistency for test exams and low consistency for video exams and traditional exams. Visually based exams showed medium consistency.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is the research considered important for educational assessment?",{"text":121,"@type":113},"It identifies where AI can complement evaluation and where it needs improvement, supporting the development of more accurate and fair assessment tools in higher education.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},601532,1792484116,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36},962090883568,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Evaluating the evaluators: A comparative study of AI and teacher assessments in Higher Education  \nTuğra Karademir Coskun 1,* , Ayfer Alper2  \n1University of Sinop, Turkey, [tkarademir@sinop.edu.tr](tkarademir@sinop.edu.tr), [https://orcid.org/0000-0003-4295-2440](https://orcid.org/0000-0003-4295-2440)[ ](https://orcid.org/0000-0003-4295-2440)2University of Ankara, Turkey, [aalper@ankara.edu.tr](aalper@ankara.edu.tr), [https://orcid.org/0000-0003-2312-631](https://orcid.org/0000-0003-2312-631)1  \nABSTRACT  \nThis study aims to examine the potential differences between teacher evaluations and artificial intelligence (AI) tool-based assessment systems in university examinations. The research has evaluated a wide spectrum of exams including numerical and verbal course exams, exams with different assessment styles (project, test exam, traditional exam), and both theoretical and practical course exams. These exams were selected using a criterion sampling method and were analyzed using BlandAltman Analysis and Intraclass Correlation Coefficient (ICC) analyses to assess how AI and teacher evaluations performed across a broad range. The research findings indicate that while there is a high level of proficiency between the total exam scores assessed by artificial intelligence and teacher evaluations; medium consistency was found in the evaluation of visually based exams, low consistency in video exams, high consistency in test exams, and low consistency in traditional exams. This research is crucial as it helps to identify specific areas where artificial intelligence can either complement or needs improvement in educational assessment, guiding the development of more accurate and fair evaluation tools. KEYWORDS: Artificial intelligence tool-based assessment systems, teacher evaluation, assessments in higher education  \n1 INTRODUCTION  \nArtificial Intelligence (AI) significantly contributes to educational processes, possessing the potential to enhance efficiency, productivity, and personalized learning experiences (Dwivedi et al., 2021; Haseski, 2019) . AI technologies allow educators to tailor teaching to meet individual student needs, enhance the learning environment, and gain deep insights into student performance (Yin, 2021) . Through the automation of tasks, the creation of personalized learning programs, and the optimized use of data, AI facilitates more accurate knowledge acquisition and targeted educational outcomes (Hinojo-Lucena et al. , 2019; Hua, 2022) . Moreover, AI supports the development of students' cognitive abilities, encourages creativity, maximizes resource utilization, and contributes to the overall advancement of teaching and learning (Jingshan, 2023) . AI is also instrumental in the design of intelligent tutoring systems, enhancing learning outcomes across various academic subjects and educational levels (Dermeval et al. , 2017) . It enables the development of innovative teaching methodologies and curriculum designs, thus creating a more engaging and effective learning environment (Yang & Wang, 2020) . Educators can use AI technologies to develop interactive and adaptive learning pathways that accommodate diverse student needs and learning styles (Tapalova & Zhiyenbayeva, 2022) . In summary, the use of AI in education is crucial for adapting to rapid technological changes, transforming teaching methodologies, increasing student engagement, and ultimately enhancing teaching standards.  \nWhen integrated into measurement and evaluation processes in education, Artificial Intelligence (AI) offers numerous benefits. AI technologies can improve assessment and feedback mechanisms, thereby enhancing students' learning outcomes (Mahligawati, 2023; Choi and McClenen, 2020) . These technologies take on functions such as identifying students' needs, providing instant  \nfeedback, and facilitating teacher intervention, which in turn boosts teaching effectiveness (Dindar et al. , 2022) . AI tools enable personalized instruction, ","cbCaidylp7ArRC3D","https://ap.wps.com/l/cbCaidylp7ArRC3D","pdf",656889,16,"English","# Abstract\n# 1 Introduction\n## AI in education and assessment\n## Benefits and limitations of human evaluation\n## AI as an alternative in evaluation","[{\"question\":\"How does the study compare AI-based assessments with teacher evaluations?\",\"answer\":\"It compares teacher evaluations and AI tool-based assessment systems on university exam scores using Bland-Altman and intraclass correlation coefficient (ICC) analyses across different exam types and assessment styles.\"},{\"question\":\"Which exam formats showed the highest and lowest consistency between AI and teachers?\",\"answer\":\"The study reports high consistency for test exams and low consistency for video exams and traditional exams. Visually based exams showed medium consistency.\"},{\"question\":\"Why is the research considered important for educational assessment?\",\"answer\":\"It identifies where AI can complement evaluation and where it needs improvement, supporting the development of more accurate and fair assessment tools in higher education.\"}]","Evaluating the Evaluators - A Comparative Study of AI and Teacher Assessments in Higher Education | PDF",1792092805]